{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# 9 Pandas分组与聚合"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  key1   key2     data1     data2\n",
      "0    a    one  0.790819 -0.551726\n",
      "1    b    one  2.328619  0.302788\n",
      "2    a    two -3.114707  1.484679\n",
      "3    b  three -1.427779 -0.874004\n",
      "4    a    two  0.117080 -0.244159\n",
      "5    b    two  0.964935 -0.872349\n",
      "6    a    one -0.302208 -0.296385\n",
      "7    a  three -1.151873 -0.845638\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "dict_obj = {'key1' : ['a', 'b', 'a', 'b',\n",
    "                      'a', 'b', 'a', 'a'],\n",
    "            'key2' : ['one', 'one', 'two', 'three',\n",
    "                      'two', 'two', 'one', 'three'],\n",
    "            'data1': np.random.randn(8),\n",
    "            'data2': np.random.randn(8)}\n",
    "df_obj = pd.DataFrame(dict_obj)\n",
    "\n",
    "print(df_obj) #两列是字符串，两列是数值"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:01:03.339816400Z",
     "start_time": "2024-07-08T02:01:02.696742300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x000001FB18F645F0>\n",
      "<class 'pandas.core.groupby.generic.DataFrameGroupBy'>\n"
     ]
    }
   ],
   "source": [
    "# dataframe根据key1进行分组，分组后的对象类型\n",
    "# 不进行聚合操作拿到的只是groupby的对象\n",
    "print(df_obj.groupby('key1'))\n",
    "print(type(df_obj.groupby('key1')))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:01:28.538005900Z",
     "start_time": "2024-07-08T02:01:28.494409700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "  key1   key2     data1     data2\n",
      "0    a    one  0.790819 -0.551726\n",
      "2    a    two -3.114707  1.484679\n",
      "4    a    two  0.117080 -0.244159\n",
      "6    a    one -0.302208 -0.296385\n",
      "7    a  three -1.151873 -0.845638\n",
      "b\n",
      "  key1   key2     data1     data2\n",
      "1    b    one  2.328619  0.302788\n",
      "3    b  three -1.427779 -0.874004\n",
      "5    b    two  0.964935 -0.872349\n"
     ]
    }
   ],
   "source": [
    "group1=df_obj.groupby('key1')\n",
    "for name,group in group1: # 遍历分组\n",
    "    print(name)\n",
    "    print(group)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-08T02:02:09.284050Z",
     "start_time": "2024-07-08T02:02:09.252973900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    0.790819\n",
      "1    2.328619\n",
      "2   -3.114707\n",
      "3   -1.427779\n",
      "4    0.117080\n",
      "5    0.964935\n",
      "6   -0.302208\n",
      "7   -1.151873\n",
      "Name: data1, dtype: float64\n",
      "--------------------------------------------------\n",
      "<class 'pandas.core.groupby.generic.SeriesGroupBy'>\n"
     ]
    }
   ],
   "source": [
    "print(df_obj['data1'])\n",
    "# dataframe的 data1 列根据 key1 进行分组\n",
    "print('-'*50)\n",
    "print(type(df_obj['data1'].groupby(df_obj['key1'])))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:04:29.206724500Z",
     "start_time": "2024-07-08T02:04:29.161517100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         data1     data2\n",
      "key1                    \n",
      "a    -0.732178 -0.090646\n",
      "b     0.621925 -0.481188\n",
      "--------------------------------------------------\n",
      "key1\n",
      "a   -0.732178\n",
      "b    0.621925\n",
      "Name: data1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# 分组运算\n",
    "grouped1 = df_obj.groupby('key1')\n",
    "print(grouped1.mean(numeric_only=True)) #不是数值类型的不会算均值\n",
    "print('-'*50)\n",
    "grouped2 = df_obj['data1'].groupby(df_obj['key1'])\n",
    "print(grouped2.mean())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:08:23.396600300Z",
     "start_time": "2024-07-08T02:08:23.360129200Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 9.1.3 按自定义的key分组（不重要，无需掌握）"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# # 按自定义key分组，列表\n",
    "self_def_key = [0, 1, 2, 3, 3, 3, 5, 7]\n",
    "print(df_obj.groupby(self_def_key).size())  #等于mysql的count\n",
    "print(df_obj.groupby(self_def_key).sum())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# 按自定义key分组，多层列表\n",
    "print(df_obj.groupby([df_obj['key1'], df_obj['key2']]).size())\n",
    "\n",
    "# # 按多个列多层分组\n",
    "print('-'*50)\n",
    "grouped2 = df_obj.groupby(['key1', 'key2'])\n",
    "print(grouped2.size())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "key2   key1\n",
      "one    a       2\n",
      "       b       1\n",
      "three  a       1\n",
      "       b       1\n",
      "two    a       2\n",
      "       b       1\n",
      "dtype: int64\n",
      "<class 'pandas.core.series.Series'>\n",
      "               data1     data2\n",
      "key2  key1                    \n",
      "one   a    -0.790356 -0.654034\n",
      "      b    -0.079730 -0.387669\n",
      "three a     0.532789  0.685627\n",
      "      b     0.740374 -0.376233\n",
      "two   a     0.365328 -0.912014\n",
      "      b    -0.027914  0.919135\n"
     ]
    }
   ],
   "source": [
    "# # 多层分组按key的顺序进行\n",
    "print('-'*50)\n",
    "grouped3 = df_obj.groupby(['key2', 'key1'])\n",
    "print(grouped3.size()) #series,类似mysql的count\n",
    "print(type(grouped3.size()))\n",
    "print(grouped3.mean())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiIndex([(  'one', 'a'),\n",
      "            (  'one', 'b'),\n",
      "            ('three', 'a'),\n",
      "            ('three', 'b'),\n",
      "            (  'two', 'a'),\n",
      "            (  'two', 'b')],\n",
      "           names=['key2', 'key1'])\n"
     ]
    }
   ],
   "source": [
    "print(grouped3.mean().index)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          data1               data2          \n",
      "key1          a         b         a         b\n",
      "key2                                         \n",
      "one   -0.790356 -0.079730 -0.654034 -0.387669\n",
      "three  0.532789  0.740374  0.685627 -0.376233\n",
      "two    0.365328 -0.027914 -0.912014  0.919135\n",
      "MultiIndex([('data1', 'a'),\n",
      "            ('data1', 'b'),\n",
      "            ('data2', 'a'),\n",
      "            ('data2', 'b')],\n",
      "           names=[None, 'key1'])\n"
     ]
    }
   ],
   "source": [
    "# unstack可以将多层索引的结果转换成单层的dataframe\n",
    "print(grouped3.mean().unstack())  #竖变横,只能变为列索引的最大的索引\n",
    "print(grouped3.mean().unstack().columns)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('one', 'a')\n",
      "  key1 key2     data1     data2\n",
      "0    a  one -0.319450 -2.084581\n",
      "6    a  one -1.261263  0.776513\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "('one', 'b')\n",
      "  key1 key2    data1     data2\n",
      "1    b  one -0.07973 -0.387669\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "('three', 'a')\n",
      "  key1   key2     data1     data2\n",
      "7    a  three  0.532789  0.685627\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "('three', 'b')\n",
      "  key1   key2     data1     data2\n",
      "3    b  three  0.740374 -0.376233\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "('two', 'a')\n",
      "  key1 key2     data1     data2\n",
      "2    a  two  0.884913 -1.122358\n",
      "4    a  two -0.154258 -0.701669\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "('two', 'b')\n",
      "  key1 key2     data1     data2\n",
      "5    b  two -0.027914  0.919135\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "# # 多层分组，根据key1 和 key2，做个性化处理\n",
    "for group_name, group_data in grouped3:\n",
    "    print(group_name)\n",
    "    print(group_data)\n",
    "    print(type(group_data))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "list(grouped3)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 9.3 按类型分组(不重要，自行查看)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "print(df_obj.dtypes)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#一列数据类型必须一致，因为现实生活中一个特征类型是一致的\n",
    "df_obj['data3']=df_obj['data2']+1\n",
    "print(df_obj)\n",
    "print(df_obj.groupby(df_obj.dtypes, axis=1).size())#按轴0没有意义\n",
    "print(df_obj.groupby(df_obj.dtypes, axis=1).sum())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#这里是自定义的列索引，用的不错\n",
    "self_column_key=[1,2,3,3,4]\n",
    "print(df_obj.groupby(self_column_key, axis=1).sum())   #通过自定义索引去让不同列相加"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 2. 通过字典分组(不重要，自行查看)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "df_obj2 = pd.DataFrame(np.random.randint(1, 10, (5,5)),\n",
    "                       columns=['a', 'b', 'c', 'd', 'e'],\n",
    "                       index=['A', 'B', 'C', 'D', 'E'])\n",
    "# 给指定某个部分的数据重新赋值为 np.NaN\n",
    "df_obj2.loc['B','b':'d']=np.NAN\n",
    "\n",
    "print(df_obj2)\n",
    "# 通过字典分组\n",
    "mapping_dict = {'a':'Python', 'b':'Python', 'c':'Java', 'd':'C', 'e':'Java'}\n",
    "print(df_obj2.groupby(mapping_dict, axis=1).size())\n",
    "print(df_obj2.groupby(mapping_dict, axis=1).count()) # df内部值非NaN的个数\n",
    "print(df_obj2.groupby(mapping_dict, axis=1).sum())  #求和不记录nan,nan当为0\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "str1='熊大'"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "str1[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# 通过函数分组\n",
    "df_obj3 = pd.DataFrame(np.random.randint(1, 10, (5,5)),\n",
    "                       columns=['a', 'b', 'c', 'd', 'ee'],\n",
    "                       index=['AA', 'BBBB', 'CC', 'D', 'EE'])\n",
    "#df_obj3\n",
    "\n",
    "def group_key(idx):\n",
    "    \"\"\"\n",
    "        idx 为列索引或行索引，默认为行索引 axis=0,传入行索引，轴为1，传入列索引\n",
    "    \"\"\"\n",
    "    #return idx\n",
    "    return len(idx)\n",
    "print(df_obj3)\n",
    "print(df_obj3.groupby(group_key,axis=0).size())\n",
    "\n",
    "# 以上自定义函数等价于\n",
    "df_obj3.groupby(len).size()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# 通过索引级别分组，可以指定某个级别进行分组\n",
    "columns = pd.MultiIndex.from_arrays([['Python', 'Java', 'Python', 'Java', 'Python'],\n",
    "                                     ['A', 'A', 'B', 'C', 'B']],\n",
    "                                    names=['language', 'index1'])\n",
    "df_obj4 = pd.DataFrame(np.random.randint(1, 10, (5, 5)), columns=columns)\n",
    "print(df_obj4)\n",
    "\n",
    "# 根据language进行分组\n",
    "print(df_obj4.groupby(level='language', axis=1).sum())\n",
    "# 根据index进行分组\n",
    "print(df_obj4.groupby(level='index1', axis=1).sum())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 9.4 聚合\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:10:48.160733800Z",
     "start_time": "2024-07-08T02:10:48.143229200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      data1  data2\n",
      "key1              \n",
      "a        28     23\n",
      "b        15     12\n",
      "      data1  data2\n",
      "key1              \n",
      "a         9      7\n",
      "b         7      6\n"
     ]
    }
   ],
   "source": [
    "dict_obj = {'key1' : ['a', 'b', 'a', 'b',\n",
    "                      'a', 'b', 'a', 'a'],\n",
    "            # 'key2' : ['one', 'one', 'two', 'three',\n",
    "            #           'two', 'two', 'one', 'three'],\n",
    "            'data1': np.random.randint(1,10, 8),\n",
    "            'data2': np.random.randint(1,10, 8)}\n",
    "df_obj=pd.DataFrame(dict_obj)\n",
    "print(df_obj.loc[:,['key1','data1','data2']].groupby(\"key1\").sum())\n",
    "print(df_obj.loc[:,['key1','data1','data2']].groupby(\"key1\").max())\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:10:51.550640200Z",
     "start_time": "2024-07-08T02:10:51.526015500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      data1  data2\n",
      "key1              \n",
      "a         2      2\n",
      "b         1      3\n",
      "--------------------------------------------------\n",
      "      data1  data2\n",
      "key1              \n",
      "a       5.6    4.6\n",
      "b       5.0    4.0\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": "     data1                                         data2                      \\\n     count mean       std  min  25%  50%  75%  max count mean       std  min   \nkey1                                                                           \na      5.0  5.6  2.701851  2.0  4.0  6.0  7.0  9.0   5.0  4.6  1.949359  2.0   \nb      3.0  5.0  3.464102  1.0  4.0  7.0  7.0  7.0   3.0  4.0  1.732051  3.0   \n\n                          \n      25%  50%  75%  max  \nkey1                      \na     4.0  4.0  6.0  7.0  \nb     3.0  3.0  4.5  6.0  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th colspan=\"8\" halign=\"left\">data1</th>\n      <th colspan=\"8\" halign=\"left\">data2</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n      <th>count</th>\n      <th>mean</th>\n      <th>std</th>\n      <th>min</th>\n      <th>25%</th>\n      <th>50%</th>\n      <th>75%</th>\n      <th>max</th>\n    </tr>\n    <tr>\n      <th>key1</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>5.0</td>\n      <td>5.6</td>\n      <td>2.701851</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>6.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n      <td>5.0</td>\n      <td>4.6</td>\n      <td>1.949359</td>\n      <td>2.0</td>\n      <td>4.0</td>\n      <td>4.0</td>\n      <td>6.0</td>\n      <td>7.0</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>3.0</td>\n      <td>5.0</td>\n      <td>3.464102</td>\n      <td>1.0</td>\n      <td>4.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>7.0</td>\n      <td>3.0</td>\n      <td>4.0</td>\n      <td>1.732051</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>3.0</td>\n      <td>4.5</td>\n      <td>6.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df_obj.loc[:,['key1','data1','data2']].groupby(\"key1\").min())\n",
    "print('-'*50)\n",
    "#平均值\n",
    "print(df_obj.loc[:,['key1','data1','data2']].groupby(\"key1\").mean())\n",
    "print('-'*50)\n",
    "df_obj.groupby(\"key1\").describe() #非常有用"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-08T02:11:39.468105500Z",
     "start_time": "2024-07-08T02:11:39.382904600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "key1     b\ndata1    9\ndata2    7\ndtype: object"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_obj.max()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-08T02:17:54.901288Z",
     "start_time": "2024-07-08T02:17:54.797660900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "  key1  data1  data2\n0    a      7      4\n1    b      7      3\n2    a      6      2\n3    b      1      6\n4    a      4      4\n5    b      7      3\n6    a      2      7\n7    a      9      6",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>key1</th>\n      <th>data1</th>\n      <th>data2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>a</td>\n      <td>7</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>b</td>\n      <td>7</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>a</td>\n      <td>6</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>b</td>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>a</td>\n      <td>4</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>b</td>\n      <td>7</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>a</td>\n      <td>2</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>a</td>\n      <td>9</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_obj"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-08T02:19:15.180801Z",
     "start_time": "2024-07-08T02:19:15.126333700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 自定义聚合函数"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  key1  data1  data2\n",
      "0    a      7      4\n",
      "1    b      7      3\n",
      "2    a      6      2\n",
      "3    b      1      6\n",
      "4    a      4      4\n",
      "5    b      7      3\n",
      "6    a      2      7\n",
      "7    a      9      6\n",
      "--------------------------------------------------\n",
      "      data1  data2\n",
      "key1              \n",
      "a         7      5\n",
      "b         6      3\n",
      "--------------------------------------------------\n",
      "      data1  data2\n",
      "key1              \n",
      "a         7      5\n",
      "b         6      3\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df_obj5 = pd.DataFrame(dict_obj)\n",
    "print(df_obj5)\n",
    "\n",
    "def peak_range(df):\n",
    "    \"\"\"\n",
    "        返回数值范围,df参数是groupby后的 那一组的dataframe\n",
    "    \"\"\"\n",
    "    #print type(df) #参数为索引所对应的记录\n",
    "    return df.max() - df.min()\n",
    "#求极值\n",
    "print('-'*50)\n",
    "print(df_obj5.loc[:,['key1','data1','data2']].groupby('key1').agg(peak_range))\n",
    "print('-'*50)\n",
    "print(df_obj5.loc[:,['key1','data1','data2']].groupby('key1').agg(lambda df : df.max() - df.min()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:18:19.217562Z",
     "start_time": "2024-07-08T02:18:19.014441300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     data1                            data2                           \n",
      "      mean       std count peak_range  mean       std count peak_range\n",
      "key1                                                                  \n",
      "a      5.6  2.701851     5          7   4.6  1.949359     5          5\n",
      "b      5.0  3.464102     3          6   4.0  1.732051     3          3\n"
     ]
    }
   ],
   "source": [
    "# 同时应用多个聚合函数\n",
    "print(df_obj.groupby('key1').agg(['mean', 'std', 'count', peak_range]))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:18:43.807256500Z",
     "start_time": "2024-07-08T02:18:43.753508800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      data1  data2\n",
      "key1              \n",
      "a       5.6      5\n",
      "b       5.0      3\n"
     ]
    }
   ],
   "source": [
    "# 每列作用不同的聚合函数\n",
    "dict_mapping = {'data1':'mean',\n",
    "                'data2':'count'}\n",
    "print(df_obj.groupby('key1').agg(dict_mapping))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:19:59.599186700Z",
     "start_time": "2024-07-08T02:19:59.580048900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 星巴克全球开店"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25600 entries, 0 to 25599\n",
      "Data columns (total 13 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   Brand           25600 non-null  object \n",
      " 1   Store Number    25600 non-null  object \n",
      " 2   Store Name      25600 non-null  object \n",
      " 3   Ownership Type  25600 non-null  object \n",
      " 4   Street Address  25598 non-null  object \n",
      " 5   City            25585 non-null  object \n",
      " 6   State/Province  25600 non-null  object \n",
      " 7   Country         25600 non-null  object \n",
      " 8   Postcode        24078 non-null  object \n",
      " 9   Phone Number    18739 non-null  object \n",
      " 10  Timezone        25600 non-null  object \n",
      " 11  Longitude       25599 non-null  float64\n",
      " 12  Latitude        25599 non-null  float64\n",
      "dtypes: float64(2), object(11)\n",
      "memory usage: 2.5+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/plain": "array([    1,   144,   108,    18,    22,     3,     4,    19,     5,\n          21,     5,     4,   102,    10,  1468,    61,    96,  2734,\n          11,    11,     3,    10,    28,   160,    21,    31,   101,\n           8,   132,   901,    28,     7,    16,   268,    73,    88,\n          17,  1237,     4,   993,   106,     8,    29,     2,     9,\n           2,   579,   234,    59,    17,    24,    12,     5,    89,\n         298,    53,    24,    11,    18,    27,   109,   102,    18,\n         130,     3,    11,   289,   326,     3,   394, 13608,    25,\n           3], dtype=int64)"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "file_path = \"./starbucks_store_worldwide.csv\"\n",
    "\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "print(df.info())  #数据总体有一个把控\n",
    "grouped=df.groupby(by='Country')\n",
    "grouped.size().values"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:21:55.112044900Z",
     "start_time": "2024-07-08T02:21:54.941315800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "       Brand  Store Number     Store Name Ownership Type     Street Address  \\\n0  Starbucks  47370-257954  Meritxell, 96       Licensed  Av. Meritxell, 96   \n\n               City State/Province Country Postcode Phone Number  \\\n0  Andorra la Vella              7      AD    AD500    376818720   \n\n                  Timezone  Longitude  Latitude  \n0  GMT+1:00 Europe/Andorra       1.53     42.51  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Brand</th>\n      <th>Store Number</th>\n      <th>Store Name</th>\n      <th>Ownership Type</th>\n      <th>Street Address</th>\n      <th>City</th>\n      <th>State/Province</th>\n      <th>Country</th>\n      <th>Postcode</th>\n      <th>Phone Number</th>\n      <th>Timezone</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Starbucks</td>\n      <td>47370-257954</td>\n      <td>Meritxell, 96</td>\n      <td>Licensed</td>\n      <td>Av. Meritxell, 96</td>\n      <td>Andorra la Vella</td>\n      <td>7</td>\n      <td>AD</td>\n      <td>AD500</td>\n      <td>376818720</td>\n      <td>GMT+1:00 Europe/Andorra</td>\n      <td>1.53</td>\n      <td>42.51</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-08T02:21:38.535234500Z",
     "start_time": "2024-07-08T02:21:38.438772300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13608\n",
      "2734\n"
     ]
    }
   ],
   "source": [
    "#任取一列求count，也可以用country列自身\n",
    "country_count = grouped[\"Brand\"].count()\n",
    "print(country_count[\"US\"])\n",
    "print(country_count[\"CN\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:41:52.400166700Z",
     "start_time": "2024-07-08T02:41:52.378531400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "<BarContainer object of 31 artists>"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 1600x800 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#中国不同省份的星巴克数量，图像绘制\n",
    "import matplotlib.pyplot as plt\n",
    "country_cn=df[df['Country']=='CN']\n",
    "china_province=country_cn.groupby(by=\"State/Province\").count()['Brand'].sort_values(ascending=False) #中国不同省份的星巴克数量，降序排序\n",
    "plt.figure(figsize=(20,10),dpi=80)\n",
    "# print(type(china_province))\n",
    "# print(china_province.index)\n",
    "plt.bar(china_province.index,china_province.values)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:43:39.094305800Z",
     "start_time": "2024-07-08T02:43:37.620308100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "Brand              object\nStore Number       object\nStore Name         object\nOwnership Type     object\nStreet Address     object\nCity               object\nState/Province     object\nCountry            object\nPostcode           object\nPhone Number       object\nTimezone           object\nLongitude         float64\nLatitude          float64\ndtype: object"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_cn.dtypes"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "          Brand  Store Number                         Store Name  \\\n3062  Starbucks  48862-265746          Changshu Fangta Street-GF   \n3063  Starbucks  48238-258108  CZ Wujin Kaia Lomo Plaza Store-GF   \n3064  Starbucks  26608-244608        Nanjing Sun Palace Store-GF   \n3065  Starbucks  48586-263487  NT Haimen Zhongnan Plaza Store-GF   \n3066  Starbucks  49404-268987    NT Rudong wenfeng Dept Store-GF   \n...         ...           ...                                ...   \n3411  Starbucks  24743-238567                          镇江丹阳吾悦广场店   \n3412  Starbucks  24065-234944                            丹阳金鹰天地店   \n3413  Starbucks  24740-238713                         泰州靖江泰和国际城店   \n3414  Starbucks  24336-236945                         泰州靖江文峰大世界店   \n3415  Starbucks  47324-257587                          扬州高邮世贸广场店   \n\n     Ownership Type                               Street Address       City  \\\n3062  Joint Venture              Fangta Street and HeDong Street   Changshu   \n3063  Joint Venture  Huayuan St & Yongsheng Road, Wujin District  Changzhou   \n3064  Joint Venture                    No.3 yangguang Rd, Xuanwu    Nanjing   \n3065  Joint Venture    Zhangjian Avenue&Nanhai Road, Haimen City    Nantong   \n3066  Joint Venture              No.19 North Qingyuan Rd, Rudong    Nantong   \n...             ...                                          ...        ...   \n3411  Joint Venture                     丹阳市开发区, 新民东路99号10#-102铺位        镇江市   \n3412  Joint Venture                                  丹阳市, 金鹰天地广场        镇江市   \n3413  Joint Venture                           车站路77号泰和国际城C1-01铺位        靖江市   \n3414  Joint Venture                  人民北路1号文峰大世界1层1-2单元, 通湖路333号        靖江市   \n3415  Joint Venture            江苏省扬州高邮市, 世贸广场一层A005/A006/A1015铺位        高邮市   \n\n     State/Province Country Postcode Phone Number                Timezone  \\\n3062             32      CN   215500          NaN  GMT+08:00 Asia/Beijing   \n3063             32      CN   213000          NaN  GMT+08:00 Asia/Beijing   \n3064             32      CN   210000          NaN  GMT+08:00 Asia/Beijing   \n3065             32      CN   226000          NaN  GMT+08:00 Asia/Beijing   \n3066             32      CN   226000          NaN  GMT+08:00 Asia/Beijing   \n...             ...     ...      ...          ...                     ...   \n3411             32      CN      NaN          NaN  GMT+08:00 Asia/Beijing   \n3412             32      CN   212300          NaN  GMT+08:00 Asia/Beijing   \n3413             32      CN   214500          NaN  GMT+08:00 Asia/Beijing   \n3414             32      CN   214500          NaN  GMT+08:00 Asia/Beijing   \n3415             32      CN   225600          NaN  GMT+08:00 Asia/Beijing   \n\n      Longitude  Latitude  \n3062     120.75     31.64  \n3063     119.95     31.69  \n3064     118.81     32.06  \n3065     121.17     31.88  \n3066     121.18     32.31  \n...         ...       ...  \n3411     119.61     32.00  \n3412     119.58     31.99  \n3413     120.26     32.01  \n3414     120.27     32.02  \n3415     119.46     32.79  \n\n[354 rows x 13 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Brand</th>\n      <th>Store Number</th>\n      <th>Store Name</th>\n      <th>Ownership Type</th>\n      <th>Street Address</th>\n      <th>City</th>\n      <th>State/Province</th>\n      <th>Country</th>\n      <th>Postcode</th>\n      <th>Phone Number</th>\n      <th>Timezone</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>3062</th>\n      <td>Starbucks</td>\n      <td>48862-265746</td>\n      <td>Changshu Fangta Street-GF</td>\n      <td>Joint Venture</td>\n      <td>Fangta Street and HeDong Street</td>\n      <td>Changshu</td>\n      <td>32</td>\n      <td>CN</td>\n      <td>215500</td>\n      <td>NaN</td>\n      <td>GMT+08:00 Asia/Beijing</td>\n      <td>120.75</td>\n      <td>31.64</td>\n    </tr>\n    <tr>\n      <th>3063</th>\n      <td>Starbucks</td>\n      <td>48238-258108</td>\n      <td>CZ Wujin Kaia Lomo Plaza Store-GF</td>\n      <td>Joint Venture</td>\n      <td>Huayuan St &amp; Yongsheng Road, Wujin District</td>\n      <td>Changzhou</td>\n      <td>32</td>\n      <td>CN</td>\n      <td>213000</td>\n      <td>NaN</td>\n      <td>GMT+08:00 Asia/Beijing</td>\n      <td>119.95</td>\n      <td>31.69</td>\n    </tr>\n    <tr>\n      <th>3064</th>\n      <td>Starbucks</td>\n      <td>26608-244608</td>\n      <td>Nanjing Sun Palace Store-GF</td>\n      <td>Joint Venture</td>\n      <td>No.3 yangguang Rd, Xuanwu</td>\n      <td>Nanjing</td>\n      <td>32</td>\n      <td>CN</td>\n      <td>210000</td>\n      <td>NaN</td>\n      <td>GMT+08:00 Asia/Beijing</td>\n      <td>118.81</td>\n      <td>32.06</td>\n    </tr>\n    <tr>\n      <th>3065</th>\n      <td>Starbucks</td>\n      <td>48586-263487</td>\n      <td>NT Haimen Zhongnan Plaza Store-GF</td>\n      <td>Joint Venture</td>\n      <td>Zhangjian Avenue&amp;Nanhai Road, Haimen City</td>\n      <td>Nantong</td>\n      <td>32</td>\n      <td>CN</td>\n      <td>226000</td>\n      <td>NaN</td>\n      <td>GMT+08:00 Asia/Beijing</td>\n      <td>121.17</td>\n      <td>31.88</td>\n    </tr>\n    <tr>\n      <th>3066</th>\n      <td>Starbucks</td>\n      <td>49404-268987</td>\n      <td>NT Rudong wenfeng Dept Store-GF</td>\n      <td>Joint Venture</td>\n      <td>No.19 North Qingyuan Rd, Rudong</td>\n      <td>Nantong</td>\n      <td>32</td>\n      <td>CN</td>\n      <td>226000</td>\n      <td>NaN</td>\n      <td>GMT+08:00 Asia/Beijing</td>\n      <td>121.18</td>\n      <td>32.31</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3411</th>\n      <td>Starbucks</td>\n      <td>24743-238567</td>\n      <td>镇江丹阳吾悦广场店</td>\n      <td>Joint Venture</td>\n      <td>丹阳市开发区, 新民东路99号10#-102铺位</td>\n      <td>镇江市</td>\n      <td>32</td>\n      <td>CN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>GMT+08:00 Asia/Beijing</td>\n      <td>119.61</td>\n      <td>32.00</td>\n    </tr>\n    <tr>\n      <th>3412</th>\n      <td>Starbucks</td>\n      <td>24065-234944</td>\n      <td>丹阳金鹰天地店</td>\n      <td>Joint Venture</td>\n      <td>丹阳市, 金鹰天地广场</td>\n      <td>镇江市</td>\n      <td>32</td>\n      <td>CN</td>\n      <td>212300</td>\n      <td>NaN</td>\n      <td>GMT+08:00 Asia/Beijing</td>\n      <td>119.58</td>\n      <td>31.99</td>\n    </tr>\n    <tr>\n      <th>3413</th>\n      <td>Starbucks</td>\n      <td>24740-238713</td>\n      <td>泰州靖江泰和国际城店</td>\n      <td>Joint Venture</td>\n      <td>车站路77号泰和国际城C1-01铺位</td>\n      <td>靖江市</td>\n      <td>32</td>\n      <td>CN</td>\n      <td>214500</td>\n      <td>NaN</td>\n      <td>GMT+08:00 Asia/Beijing</td>\n      <td>120.26</td>\n      <td>32.01</td>\n    </tr>\n    <tr>\n      <th>3414</th>\n      <td>Starbucks</td>\n      <td>24336-236945</td>\n      <td>泰州靖江文峰大世界店</td>\n      <td>Joint Venture</td>\n      <td>人民北路1号文峰大世界1层1-2单元, 通湖路333号</td>\n      <td>靖江市</td>\n      <td>32</td>\n      <td>CN</td>\n      <td>214500</td>\n      <td>NaN</td>\n      <td>GMT+08:00 Asia/Beijing</td>\n      <td>120.27</td>\n      <td>32.02</td>\n    </tr>\n    <tr>\n      <th>3415</th>\n      <td>Starbucks</td>\n      <td>47324-257587</td>\n      <td>扬州高邮世贸广场店</td>\n      <td>Joint Venture</td>\n      <td>江苏省扬州高邮市, 世贸广场一层A005/A006/A1015铺位</td>\n      <td>高邮市</td>\n      <td>32</td>\n      <td>CN</td>\n      <td>225600</td>\n      <td>NaN</td>\n      <td>GMT+08:00 Asia/Beijing</td>\n      <td>119.46</td>\n      <td>32.79</td>\n    </tr>\n  </tbody>\n</table>\n<p>354 rows × 13 columns</p>\n</div>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country_cn[country_cn['State/Province']=='32']\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:45:53.854975600Z",
     "start_time": "2024-07-08T02:45:53.655700700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "                        Brand\nCountry State/Province       \nAD      7                   1\nAE      AJ                  2\n        AZ                 48\n        DU                 82\n        FU                  2\n...                       ...\nUS      WV                 25\n        WY                 23\nVN      HN                  6\n        SG                 19\nZA      GT                  3\n\n[545 rows x 1 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>Brand</th>\n    </tr>\n    <tr>\n      <th>Country</th>\n      <th>State/Province</th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>AD</th>\n      <th>7</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">AE</th>\n      <th>AJ</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>AZ</th>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>DU</th>\n      <td>82</td>\n    </tr>\n    <tr>\n      <th>FU</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">US</th>\n      <th>WV</th>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>WY</th>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">VN</th>\n      <th>HN</th>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>SG</th>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>ZA</th>\n      <th>GT</th>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>545 rows × 1 columns</p>\n</div>"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped1 = df[[\"Brand\"]].groupby(by=[df[\"Country\"], df[\"State/Province\"]]).count()\n",
    "grouped1"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:46:33.378860600Z",
     "start_time": "2024-07-08T02:46:33.337502800Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "MultiIndex([('AD',  '7'),\n            ('AE', 'AJ'),\n            ('AE', 'AZ'),\n            ('AE', 'DU'),\n            ('AE', 'FU'),\n            ('AE', 'RK'),\n            ('AE', 'SH'),\n            ('AE', 'UQ'),\n            ('AR',  'B'),\n            ('AR',  'C'),\n            ...\n            ('US', 'UT'),\n            ('US', 'VA'),\n            ('US', 'VT'),\n            ('US', 'WA'),\n            ('US', 'WI'),\n            ('US', 'WV'),\n            ('US', 'WY'),\n            ('VN', 'HN'),\n            ('VN', 'SG'),\n            ('ZA', 'GT')],\n           names=['Country', 'State/Province'], length=545)"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped1.index #多次group后得到的是层级索引"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-08T02:47:05.074536300Z",
     "start_time": "2024-07-08T02:47:05.038228Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "                        Brand  Store Number  Store Name  Ownership Type  \\\nCountry State/Province                                                    \nAD      7                   1             1           1               1   \nAE      AJ                  2             2           2               2   \n        AZ                 48            48          48              48   \n        DU                 82            82          82              82   \n        FU                  2             2           2               2   \n...                       ...           ...         ...             ...   \nUS      WV                 25            25          25              25   \n        WY                 23            23          23              23   \nVN      HN                  6             6           6               6   \n        SG                 19            19          19              19   \nZA      GT                  3             3           3               3   \n\n                        Street Address  City  Postcode  Phone Number  \\\nCountry State/Province                                                 \nAD      7                            1     1         1             1   \nAE      AJ                           2     2         0             0   \n        AZ                          48    48         7            20   \n        DU                          82    82        16            50   \n        FU                           2     2         1             0   \n...                                ...   ...       ...           ...   \nUS      WV                          25    25        25            23   \n        WY                          23    23        23            22   \nVN      HN                           6     6         6             6   \n        SG                          19    19        19            17   \nZA      GT                           3     3         3             2   \n\n                        Timezone  Longitude  Latitude  \nCountry State/Province                                 \nAD      7                      1          1         1  \nAE      AJ                     2          2         2  \n        AZ                    48         48        48  \n        DU                    82         82        82  \n        FU                     2          2         2  \n...                          ...        ...       ...  \nUS      WV                    25         25        25  \n        WY                    23         23        23  \nVN      HN                     6          6         6  \n        SG                    19         19        19  \nZA      GT                     3          3         3  \n\n[545 rows x 11 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>Brand</th>\n      <th>Store Number</th>\n      <th>Store Name</th>\n      <th>Ownership Type</th>\n      <th>Street Address</th>\n      <th>City</th>\n      <th>Postcode</th>\n      <th>Phone Number</th>\n      <th>Timezone</th>\n      <th>Longitude</th>\n      <th>Latitude</th>\n    </tr>\n    <tr>\n      <th>Country</th>\n      <th>State/Province</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>AD</th>\n      <th>7</th>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">AE</th>\n      <th>AJ</th>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>AZ</th>\n      <td>48</td>\n      <td>48</td>\n      <td>48</td>\n      <td>48</td>\n      <td>48</td>\n      <td>48</td>\n      <td>7</td>\n      <td>20</td>\n      <td>48</td>\n      <td>48</td>\n      <td>48</td>\n    </tr>\n    <tr>\n      <th>DU</th>\n      <td>82</td>\n      <td>82</td>\n      <td>82</td>\n      <td>82</td>\n      <td>82</td>\n      <td>82</td>\n      <td>16</td>\n      <td>50</td>\n      <td>82</td>\n      <td>82</td>\n      <td>82</td>\n    </tr>\n    <tr>\n      <th>FU</th>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n      <td>1</td>\n      <td>0</td>\n      <td>2</td>\n      <td>2</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">US</th>\n      <th>WV</th>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n      <td>23</td>\n      <td>25</td>\n      <td>25</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>WY</th>\n      <td>23</td>\n      <td>23</td>\n      <td>23</td>\n      <td>23</td>\n      <td>23</td>\n      <td>23</td>\n      <td>23</td>\n      <td>22</td>\n      <td>23</td>\n      <td>23</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th rowspan=\"2\" valign=\"top\">VN</th>\n      <th>HN</th>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>SG</th>\n      <td>19</td>\n      <td>19</td>\n      <td>19</td>\n      <td>19</td>\n      <td>19</td>\n      <td>19</td>\n      <td>19</td>\n      <td>17</td>\n      <td>19</td>\n      <td>19</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>ZA</th>\n      <th>GT</th>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n      <td>2</td>\n      <td>3</td>\n      <td>3</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n<p>545 rows × 11 columns</p>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped2 = df.groupby(by=[df[\"Country\"], df[\"State/Province\"]]).count()\n",
    "grouped2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:48:12.129328200Z",
     "start_time": "2024-07-08T02:48:12.082284300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "#这里可以看出32对应的省是江苏\n",
    "country_cn[country_cn['State/Province']=='32']"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 设置索引"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2178018665744\n"
     ]
    },
    {
     "data": {
      "text/plain": "   a  b    c  d\n0  0  7  one  h\n1  1  6  one  j\n2  2  5  one  k\n3  3  4  two  l\n4  4  3  two  m\n5  5  2  two  n\n6  6  1  two  o",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>7</td>\n      <td>one</td>\n      <td>h</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>6</td>\n      <td>one</td>\n      <td>j</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>5</td>\n      <td>one</td>\n      <td>k</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>4</td>\n      <td>two</td>\n      <td>l</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>3</td>\n      <td>two</td>\n      <td>m</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>5</td>\n      <td>2</td>\n      <td>two</td>\n      <td>n</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>6</td>\n      <td>1</td>\n      <td>two</td>\n      <td>o</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#索引中单项不可变，但是整体可以换掉\n",
    "a = pd.DataFrame({'a': range(7),'b': range(7, 0, -1),\n",
    "                  'c': ['one','one','one','two','two','two', 'two'],\n",
    "                  'd': list(\"hjklmno\")})\n",
    "print(id(a))\n",
    "a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:49:07.530135400Z",
     "start_time": "2024-07-08T02:49:07.465502700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b    c  d\n",
      "0  0  7  one  h\n",
      "1  1  6  one  j\n",
      "2  2  5  one  k\n",
      "3  3  4  two  l\n",
      "4  4  3  two  m\n",
      "5  5  2  two  n\n",
      "6  6  1  two  o\n",
      "--------------------------------------------------\n",
      "2178018665744\n"
     ]
    },
    {
     "data": {
      "text/plain": "   a  b    c  d\na  0  7  one  h\nb  1  6  one  j\nc  2  5  one  k\nd  3  4  two  l\ne  4  3  two  m\nf  5  2  two  n\ng  6  1  two  o",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>0</td>\n      <td>7</td>\n      <td>one</td>\n      <td>h</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>1</td>\n      <td>6</td>\n      <td>one</td>\n      <td>j</td>\n    </tr>\n    <tr>\n      <th>c</th>\n      <td>2</td>\n      <td>5</td>\n      <td>one</td>\n      <td>k</td>\n    </tr>\n    <tr>\n      <th>d</th>\n      <td>3</td>\n      <td>4</td>\n      <td>two</td>\n      <td>l</td>\n    </tr>\n    <tr>\n      <th>e</th>\n      <td>4</td>\n      <td>3</td>\n      <td>two</td>\n      <td>m</td>\n    </tr>\n    <tr>\n      <th>f</th>\n      <td>5</td>\n      <td>2</td>\n      <td>two</td>\n      <td>n</td>\n    </tr>\n    <tr>\n      <th>g</th>\n      <td>6</td>\n      <td>1</td>\n      <td>two</td>\n      <td>o</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=a.copy()\n",
    "a.index=list('abcdefg')  #a的索引变了，a.index更换索引\n",
    "print(c)\n",
    "# a.columns=list('ABCD')  #a的列名变了，a.columns更换列名\n",
    "print('-'*50)\n",
    "print(id(a))\n",
    "a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:49:54.527557500Z",
     "start_time": "2024-07-08T02:49:54.446460200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "(7, 4)"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.values.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-04-11T08:42:05.057905400Z",
     "start_time": "2024-04-11T08:42:05.034914200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "   a  b    c  d\n",
      "0  0  7  one  h\n",
      "1  1  6  one  j\n",
      "2  2  5  one  k\n",
      "3  3  4  two  l\n",
      "4  4  3  two  m\n",
      "5  5  2  two  n\n",
      "6  6  1  two  o\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": "    a   b    c    d\na NaN NaN  NaN  NaN\nb NaN NaN  NaN  NaN\nc NaN NaN  NaN  NaN\nd NaN NaN  NaN  NaN\ne NaN NaN  NaN  NaN\nf NaN NaN  NaN  NaN\ng NaN NaN  NaN  NaN",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n      <th>c</th>\n      <th>d</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>a</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>b</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>c</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>d</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>e</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>f</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>g</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('-'*50)\n",
    "b=c.reindex(list('abcdefg'))  #返回一个新的df，索引是设置了c的索引后，c索引不变,b是没有值\n",
    "print(c)\n",
    "print('-'*50)\n",
    "b"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-04-11T08:42:41.528179Z",
     "start_time": "2024-04-11T08:42:41.459219700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   a  b    c  d\n",
      "0  0  7  one  h\n",
      "1  1  6  one  j\n",
      "2  2  5  one  k\n",
      "3  3  4  two  l\n",
      "4  4  3  two  m\n",
      "5  5  2  two  n\n",
      "6  6  1  two  o\n",
      "--------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/plain": "       a  b\nc   d      \none h  0  7\n    j  1  6\n    k  2  5\ntwo l  3  4\n    m  4  3\n    n  5  2\n    o  6  1",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>a</th>\n      <th>b</th>\n    </tr>\n    <tr>\n      <th>c</th>\n      <th>d</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">one</th>\n      <th>h</th>\n      <td>0</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>j</th>\n      <td>1</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>k</th>\n      <td>2</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">two</th>\n      <th>l</th>\n      <td>3</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>m</th>\n      <td>4</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>n</th>\n      <td>5</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>o</th>\n      <td>6</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#让某些列变为索引，让c列，d列数据变为索引\n",
    "a=c.copy()\n",
    "print(a)\n",
    "print('-'*50)\n",
    "a.set_index(['c','d'],inplace=True)#a没变，返回修改后的df,set_index要熟练掌握\n",
    "a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:53:05.665605700Z",
     "start_time": "2024-07-08T02:53:05.637878600Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 时间序列"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04',\n               '2019-01-05', '2019-01-06', '2019-01-07', '2019-01-08',\n               '2019-01-09', '2019-01-10', '2019-01-11', '2019-01-12',\n               '2019-01-13', '2019-01-14', '2019-01-15', '2019-01-16',\n               '2019-01-17', '2019-01-18', '2019-01-19', '2019-01-20',\n               '2019-01-21', '2019-01-22', '2019-01-23', '2019-01-24',\n               '2019-01-25', '2019-01-26', '2019-01-27', '2019-01-28',\n               '2019-01-29', '2019-01-30', '2019-01-31', '2019-02-01'],\n              dtype='datetime64[ns]', freq='D')"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20190101\", end=\"20190201\")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:54:19.046529700Z",
     "start_time": "2024-07-08T02:54:19.036227900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2024-07-08', '2024-07-09', '2024-07-10', '2024-07-11',\n               '2024-07-12', '2024-07-15', '2024-07-16', '2024-07-17',\n               '2024-07-18', '2024-07-19'],\n              dtype='datetime64[ns]', freq='B')"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20240708\",periods=10,freq='B')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:54:46.386773700Z",
     "start_time": "2024-07-08T02:54:46.327629500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2019-01-31', '2019-02-28', '2019-03-31', '2019-04-30',\n               '2019-05-31', '2019-06-30', '2019-07-31', '2019-08-31',\n               '2019-09-30', '2019-10-31'],\n              dtype='datetime64[ns]', freq='ME')"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20190101\",periods=10,freq='ME')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:55:16.027559400Z",
     "start_time": "2024-07-08T02:55:16.000035Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2019-01-01', '2019-02-01', '2019-03-01', '2019-04-01',\n               '2019-05-01', '2019-06-01', '2019-07-01', '2019-08-01',\n               '2019-09-01', '2019-10-01'],\n              dtype='datetime64[ns]', freq='MS')"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20190101\",periods=10,freq='MS')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:55:27.319661800Z",
     "start_time": "2024-07-08T02:55:27.266895100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "DatetimeIndex(['2024-07-07', '2024-07-14', '2024-07-21', '2024-07-28',\n               '2024-08-04', '2024-08-11', '2024-08-18', '2024-08-25',\n               '2024-09-01', '2024-09-08'],\n              dtype='datetime64[ns]', freq='W-SUN')"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range(start=\"20240707\",periods=10,freq='W')  #拿每周的周日生成"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:55:40.859053100Z",
     "start_time": "2024-07-08T02:55:40.810658Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "0     3/11/2000\n1     3/12/2000\n2     3/13/2000\n3     3/11/2000\n4     3/12/2000\n5     3/13/2000\n6     3/11/2000\n7     3/12/2000\n8     3/13/2000\n9     3/11/2000\n10    3/12/2000\n11    3/13/2000\n12    3/11/2000\n13    3/12/2000\n14    3/13/2000\ndtype: object"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000'] * 5)\n",
    "s"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:55:59.706367100Z",
     "start_time": "2024-07-08T02:55:59.678311900Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "#timeit可以统计执行耗时，to_datetime把字符串转为时间格式\n",
    "date_ser=pd.to_datetime(s)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T02:58:08.668007Z",
     "start_time": "2024-07-08T02:58:08.632772700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "0     3\n1     3\n2     3\n3     3\n4     3\n5     3\n6     3\n7     3\n8     3\n9     3\n10    3\n11    3\n12    3\n13    3\n14    3\ndtype: int32"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_ser.dt.month"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-08T02:59:21.363356500Z",
     "start_time": "2024-07-08T02:59:21.284621700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 249737 entries, 0 to 249736\n",
      "Data columns (total 9 columns):\n",
      " #   Column     Non-Null Count   Dtype  \n",
      "---  ------     --------------   -----  \n",
      " 0   lat        249737 non-null  float64\n",
      " 1   lng        249737 non-null  float64\n",
      " 2   desc       249737 non-null  object \n",
      " 3   zip        219391 non-null  float64\n",
      " 4   title      249737 non-null  object \n",
      " 5   timeStamp  249737 non-null  object \n",
      " 6   twp        249644 non-null  object \n",
      " 7   addr       249737 non-null  object \n",
      " 8   e          249737 non-null  int64  \n",
      "dtypes: float64(3), int64(1), object(5)\n",
      "memory usage: 17.1+ MB\n"
     ]
    }
   ],
   "source": [
    "#实战美国911数据\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "# 把时间字符串转为时间类型设置为索引\n",
    "df = pd.read_csv(\"./911.csv\")\n",
    "df.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T03:05:09.733177Z",
     "start_time": "2024-07-08T03:05:07.531485600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "outputs": [
    {
     "data": {
      "text/plain": "                           lat        lng  \\\ntimeStamp                                   \n2015-12-10 17:10:52  40.297876 -75.581294   \n2015-12-10 17:29:21  40.258061 -75.264680   \n2015-12-10 14:39:21  40.121182 -75.351975   \n2015-12-10 16:47:36  40.116153 -75.343513   \n2015-12-10 16:56:52  40.251492 -75.603350   \n2015-12-10 15:39:04  40.253473 -75.283245   \n2015-12-10 16:46:48  40.182111 -75.127795   \n2015-12-10 16:17:05  40.217286 -75.405182   \n2015-12-10 16:51:42  40.289027 -75.399590   \n2015-12-10 17:35:41  40.102398 -75.291458   \n\n                                                                  desc  \\\ntimeStamp                                                                \n2015-12-10 17:10:52  REINDEER CT & DEAD END;  NEW HANOVER; Station ...   \n2015-12-10 17:29:21  BRIAR PATH & WHITEMARSH LN;  HATFIELD TOWNSHIP...   \n2015-12-10 14:39:21  HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...   \n2015-12-10 16:47:36  AIRY ST & SWEDE ST;  NORRISTOWN; Station 308A;...   \n2015-12-10 16:56:52  CHERRYWOOD CT & DEAD END;  LOWER POTTSGROVE; S...   \n2015-12-10 15:39:04  CANNON AVE & W 9TH ST;  LANSDALE; Station 345;...   \n2015-12-10 16:46:48  LAUREL AVE & OAKDALE AVE;  HORSHAM; Station 35...   \n2015-12-10 16:17:05  COLLEGEVILLE RD & LYWISKI RD;  SKIPPACK; Stati...   \n2015-12-10 16:51:42  MAIN ST & OLD SUMNEYTOWN PIKE;  LOWER SALFORD;...   \n2015-12-10 17:35:41  BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD; PLYM...   \n\n                         zip                        title                twp  \\\ntimeStamp                                                                      \n2015-12-10 17:10:52  19525.0       EMS: BACK PAINS/INJURY        NEW HANOVER   \n2015-12-10 17:29:21  19446.0      EMS: DIABETIC EMERGENCY  HATFIELD TOWNSHIP   \n2015-12-10 14:39:21  19401.0          Fire: GAS-ODOR/LEAK         NORRISTOWN   \n2015-12-10 16:47:36  19401.0       EMS: CARDIAC EMERGENCY         NORRISTOWN   \n2015-12-10 16:56:52      NaN               EMS: DIZZINESS   LOWER POTTSGROVE   \n2015-12-10 15:39:04  19446.0             EMS: HEAD INJURY           LANSDALE   \n2015-12-10 16:46:48  19044.0         EMS: NAUSEA/VOMITING            HORSHAM   \n2015-12-10 16:17:05  19426.0   EMS: RESPIRATORY EMERGENCY           SKIPPACK   \n2015-12-10 16:51:42  19438.0        EMS: SYNCOPAL EPISODE      LOWER SALFORD   \n2015-12-10 17:35:41  19462.0  Traffic: VEHICLE ACCIDENT -           PLYMOUTH   \n\n                                                         addr  e     cate  \ntimeStamp                                                                  \n2015-12-10 17:10:52                    REINDEER CT & DEAD END  1      EMS  \n2015-12-10 17:29:21                BRIAR PATH & WHITEMARSH LN  1      EMS  \n2015-12-10 14:39:21                                  HAWS AVE  1     Fire  \n2015-12-10 16:47:36                        AIRY ST & SWEDE ST  1      EMS  \n2015-12-10 16:56:52                  CHERRYWOOD CT & DEAD END  1      EMS  \n2015-12-10 15:39:04                     CANNON AVE & W 9TH ST  1      EMS  \n2015-12-10 16:46:48                  LAUREL AVE & OAKDALE AVE  1      EMS  \n2015-12-10 16:17:05              COLLEGEVILLE RD & LYWISKI RD  1      EMS  \n2015-12-10 16:51:42             MAIN ST & OLD SUMNEYTOWN PIKE  1      EMS  \n2015-12-10 17:35:41  BLUEROUTE  & RAMP I476 NB TO CHEMICAL RD  1  Traffic  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>lat</th>\n      <th>lng</th>\n      <th>desc</th>\n      <th>zip</th>\n      <th>title</th>\n      <th>twp</th>\n      <th>addr</th>\n      <th>e</th>\n      <th>cate</th>\n    </tr>\n    <tr>\n      <th>timeStamp</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2015-12-10 17:10:52</th>\n      <td>40.297876</td>\n      <td>-75.581294</td>\n      <td>REINDEER CT &amp; DEAD END;  NEW HANOVER; Station ...</td>\n      <td>19525.0</td>\n      <td>EMS: BACK PAINS/INJURY</td>\n      <td>NEW HANOVER</td>\n      <td>REINDEER CT &amp; DEAD END</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 17:29:21</th>\n      <td>40.258061</td>\n      <td>-75.264680</td>\n      <td>BRIAR PATH &amp; WHITEMARSH LN;  HATFIELD TOWNSHIP...</td>\n      <td>19446.0</td>\n      <td>EMS: DIABETIC EMERGENCY</td>\n      <td>HATFIELD TOWNSHIP</td>\n      <td>BRIAR PATH &amp; WHITEMARSH LN</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 14:39:21</th>\n      <td>40.121182</td>\n      <td>-75.351975</td>\n      <td>HAWS AVE; NORRISTOWN; 2015-12-10 @ 14:39:21-St...</td>\n      <td>19401.0</td>\n      <td>Fire: GAS-ODOR/LEAK</td>\n      <td>NORRISTOWN</td>\n      <td>HAWS AVE</td>\n      <td>1</td>\n      <td>Fire</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 16:47:36</th>\n      <td>40.116153</td>\n      <td>-75.343513</td>\n      <td>AIRY ST &amp; SWEDE ST;  NORRISTOWN; Station 308A;...</td>\n      <td>19401.0</td>\n      <td>EMS: CARDIAC EMERGENCY</td>\n      <td>NORRISTOWN</td>\n      <td>AIRY ST &amp; SWEDE ST</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 16:56:52</th>\n      <td>40.251492</td>\n      <td>-75.603350</td>\n      <td>CHERRYWOOD CT &amp; DEAD END;  LOWER POTTSGROVE; S...</td>\n      <td>NaN</td>\n      <td>EMS: DIZZINESS</td>\n      <td>LOWER POTTSGROVE</td>\n      <td>CHERRYWOOD CT &amp; DEAD END</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 15:39:04</th>\n      <td>40.253473</td>\n      <td>-75.283245</td>\n      <td>CANNON AVE &amp; W 9TH ST;  LANSDALE; Station 345;...</td>\n      <td>19446.0</td>\n      <td>EMS: HEAD INJURY</td>\n      <td>LANSDALE</td>\n      <td>CANNON AVE &amp; W 9TH ST</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 16:46:48</th>\n      <td>40.182111</td>\n      <td>-75.127795</td>\n      <td>LAUREL AVE &amp; OAKDALE AVE;  HORSHAM; Station 35...</td>\n      <td>19044.0</td>\n      <td>EMS: NAUSEA/VOMITING</td>\n      <td>HORSHAM</td>\n      <td>LAUREL AVE &amp; OAKDALE AVE</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 16:17:05</th>\n      <td>40.217286</td>\n      <td>-75.405182</td>\n      <td>COLLEGEVILLE RD &amp; LYWISKI RD;  SKIPPACK; Stati...</td>\n      <td>19426.0</td>\n      <td>EMS: RESPIRATORY EMERGENCY</td>\n      <td>SKIPPACK</td>\n      <td>COLLEGEVILLE RD &amp; LYWISKI RD</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 16:51:42</th>\n      <td>40.289027</td>\n      <td>-75.399590</td>\n      <td>MAIN ST &amp; OLD SUMNEYTOWN PIKE;  LOWER SALFORD;...</td>\n      <td>19438.0</td>\n      <td>EMS: SYNCOPAL EPISODE</td>\n      <td>LOWER SALFORD</td>\n      <td>MAIN ST &amp; OLD SUMNEYTOWN PIKE</td>\n      <td>1</td>\n      <td>EMS</td>\n    </tr>\n    <tr>\n      <th>2015-12-10 17:35:41</th>\n      <td>40.102398</td>\n      <td>-75.291458</td>\n      <td>BLUEROUTE  &amp; RAMP I476 NB TO CHEMICAL RD; PLYM...</td>\n      <td>19462.0</td>\n      <td>Traffic: VEHICLE ACCIDENT -</td>\n      <td>PLYMOUTH</td>\n      <td>BLUEROUTE  &amp; RAMP I476 NB TO CHEMICAL RD</td>\n      <td>1</td>\n      <td>Traffic</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"timeStamp\"] = pd.to_datetime(df[\"timeStamp\"]) #转为时间类型\n",
    "\n",
    "# 添加列，表示分类\n",
    "temp_list = df[\"title\"].str.split(\": \").tolist() #二维列表\n",
    "cate_list = [i[0] for i in temp_list] #i[0]就是EMS  Fire  Traffic\n",
    "# print(cate_list)\n",
    "# print(np.array(cate_list).reshape((df.shape[0], 1)))\n",
    " #添加一列\n",
    "df[\"cate\"] = pd.DataFrame(np.array(cate_list).reshape((df.shape[0], 1)))\n",
    "\n",
    "df.set_index(\"timeStamp\", inplace=True) #设置索引，时间戳，inplace=True表示在原df上修改\n",
    "\n",
    "df.head(10)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-08T03:05:13.845740300Z",
     "start_time": "2024-07-08T03:05:12.474852Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "timeStamp\n",
      "2015-12-01    3898\n",
      "2016-01-01    6063\n",
      "2016-02-01    5413\n",
      "2016-03-01    5832\n",
      "2016-04-01    5684\n",
      "2016-05-01    5730\n",
      "2016-06-01    5720\n",
      "2016-07-01    6029\n",
      "2016-08-01    6005\n",
      "2016-09-01    5750\n",
      "2016-10-01    6039\n",
      "2016-11-01    5838\n",
      "2016-12-01    6024\n",
      "2017-01-01    6082\n",
      "2017-02-01    5417\n",
      "2017-03-01    5913\n",
      "2017-04-01    5780\n",
      "2017-05-01    5948\n",
      "2017-06-01    6030\n",
      "2017-07-01    5974\n",
      "2017-08-01    5882\n",
      "2017-09-01    3789\n",
      "Freq: MS, Name: title, dtype: int64\n",
      "timeStamp\n",
      "2015-12-01    1095\n",
      "2016-01-01    1904\n",
      "2016-02-01    1868\n",
      "2016-03-01    1589\n",
      "2016-04-01    1717\n",
      "2016-05-01    1573\n",
      "2016-06-01    1787\n",
      "2016-07-01    1898\n",
      "2016-08-01    1907\n",
      "2016-09-01    1793\n",
      "2016-10-01    1930\n",
      "2016-11-01    1765\n",
      "2016-12-01    1846\n",
      "2017-01-01    1658\n",
      "2017-02-01    1462\n",
      "2017-03-01    1634\n",
      "2017-04-01    1614\n",
      "2017-05-01    1670\n",
      "2017-06-01    1986\n",
      "2017-07-01    1754\n",
      "2017-08-01    1862\n",
      "2017-09-01    1120\n",
      "Freq: MS, Name: title, dtype: int64\n",
      "timeStamp\n",
      "2015-12-01    2923\n",
      "2016-01-01    5129\n",
      "2016-02-01    4115\n",
      "2016-03-01    3638\n",
      "2016-04-01    3886\n",
      "2016-05-01    4071\n",
      "2016-06-01    4225\n",
      "2016-07-01    4161\n",
      "2016-08-01    3992\n",
      "2016-09-01    4126\n",
      "2016-10-01    4533\n",
      "2016-11-01    4488\n",
      "2016-12-01    4292\n",
      "2017-01-01    3865\n",
      "2017-02-01    3388\n",
      "2017-03-01    4137\n",
      "2017-04-01    3662\n",
      "2017-05-01    4101\n",
      "2017-06-01    4317\n",
      "2017-07-01    4040\n",
      "2017-08-01    4009\n",
      "2017-09-01    2367\n",
      "Freq: MS, Name: title, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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Zz3HmN0Mw6KFrb+/VY2bPnt0zDfn222+HXq9He3s7Ojs7cfLkSbz11lt4//33odfrkZSUhOXLl0Oj0aC1tRUTJkxAbGwsRo0aZZI+GAwG6HQ65OfnX9JArV6dzXd1daGoqAhRUVF49dVXcfDgQUyZMgWbNm267IJ7a8mSJViyZEnPbX9/f5sffWgr/Tu6oQxqZztMiYtGXl7eOf36LDgUt328B58drEGovxcWjg2xYKVXxlZ+XmezxT4B7Jc1sZY+6Q0Cvjt4Gm//koPi2lZ4OMrx7LWDMTch6IJTW6ylX5di79FTyNBKsGZPMVbur8YXh2oxI8YHCxKDMczfzdLlXbb+/rMqqW3FstQ8fL23FJ16AwZ5O+OhSRG4eoj3H45+uFi/wgEkjQCeBVBQ3YLNR8ux8Ug5fsxuwI/ZDXB3kOHqId64dqgvEkKVkPazEYf98edVVNOCrScqseV4JfYV1cIgGN9wxoUoMTXKG1OivBCgdLjo4/tjn0zh9/0KDwcc3NVY9Nk+PLutAv/3QCLUzgoLVXf5bPHnZYt9Ativ36tv7eweHdiK4prfpg0X1bagsrHjvPvbScUIVDpgop8SQR4OCPJwQKDSAUEejvBzszf5UhZ99fPq6NKjprnznNGJ54xe7L59rLL9iqZBdwntiPYPgrNC1if96kt9/X9Lr9cjOzsbdnZ2kEi6z7vnfmPS52hvb4dC8dvfpN5ckheJRPj66697piGvXLkSEokECoUCcrkcoaGhmDJlCgDgxIkTOHHiBObPn9/z+ObmZuTl5WHcuHEm6YNer4dMJkNoaOhv36de6FVYGBgYCLFYjNtvvx0AMHz4cISEhKCoqAjl5eU986wFQUBxcTECAwPh4uJyWcfINlQ0tCOrsgmzRvhfML12spNixYLRuHlZOv614QSUjnJcH+tngUqJiC6dwSDgx2MVWLo1C3naFrgopHh8mgYLEoMH3G7v7vZSLJ4UjnsnhGHL8UqsSivsmdI6PNANCxKDMX2ID9fBM5F8bTM+SMnDdwdPo8sgICbADQ9NDMfkwWqTLesR4umIByeG48GJ4cjTNmPzEeNU5S8zS/BlZgmUjnJcPcQbM4b5ID7Eg1OzugmCgGOnG7HlRAW2HK9EVmUTAOMmJFOivDA1yhuTBqnh7mh9I+fM7aooL7x841A8+X9HsWD5Xqy9N8Em30gTWYLBIKCyyThduLg7BOwJBGta0Nh+/iZKrvYyBHs4IC7EA0FKBwR6OCCoOxBUO9v1mw2TTMlOKoGvmz183ez/8H4mmQb9fRHUzsbNqcJUTghVOSFU5YhwlRN83ez5d9XGOTk59XwuCAKUSiUOHTpkuYIuolfvaDw9PTF58mT8/PPPuOaaa1BQUICCggKMHTsWI0aMwJo1a7BgwQKsX7/+nJF/l3uMrF9qjhYAutcrvDAPJzt8dlc8bvowDY99fRgu9jIubk1E/ZogCPj1VBX+uyUbJ8ob4SiX4KFJ4bh7fChc7Qf2G1uZRIxrh/ng2mE+OF7WgFVphfj+UBn++tUhvOh8ErfHB+K2+ECrHDHUH5yqaMT72/Ow6UgZDAIQH6LEQ5MiMDbcw6xr/4apnPDQ5Ag8NDkCuVVN2HSkAhuPlOGLjGJ8kVEMTyc5pg8x/txHBysH3Bscnd6AjPxabDlRga0nKlHeYJyepHSUY/Yof0yN8sa4CE8uot8Lc+ICoW3qwH+3ZuO+NfuxfMFo7tJN1Es6vYB8bXP36MDfgsCi2laU1Laio+v8kXA+rgoM9nHpHh3o2D060AFBSke4Ogzsc5o/IhKJ4GQnhZOdFCGejn94X4NBQF1r5zlB4pG802gw2CFf24xjpxuxJ7/2nMfIpWKEejqeFSQ6ItTT+C8votgejUYDFxcXrFixAgsXLgQA5ObmQqlUQqlUWrS2Xg9/+Oijj3DXXXfhiSeegFgsxrJly+Dn54dly5ZhwYIFePnll3s6ecblHiPrtzOnGiIRMC78j9cj9HOzx2d3xeGmj9Jx/5r9+PzueIwMsux/CiKi3xMEAbtza/DGliwcKqmHnVSMe8aH4L4JYfBwsrN0ef1OtK8rXr8pBk9OH4yv9hZjTXoR3tqWg/e35+KaocYpysMD3S1dplU4UlqP937NxZYTlQCApEgVFk8MR1xI3/+tDFc7469XOeOvV0Ugu7IJG4+UY+ORMny2pwif7SmCytkO1wzxxrXDfDEqyN0mR54AQHNHF3ZkabHlRAV+PVWFpu5ROUEeDrhnfAimRntjRKD7gAtOTWHxpHBUNrVjzZ5i/O2bI3j7llib/T0iuhJtnXrsya/BjmwtUnO0KKxugUHIPuc+MokIAe4OGBN2ZnSgY/foQAcEKB14EaMPiMUieDjZwcPJDoO8jV8b5tLeM0hKEARomzqQq21GvrYF+doW5GmbkV/djB+PVeD3S++pne1+CxBVTgjrDhQ5GtF6SaVSbNy4EY888gjefPNN6PV6eHp64osvvrB0ab0PC0NDQ7F9+/bzvq7RaJCenn7Bx1zuMbJueoOAXTlaDPF17dWb6HC1M1YsGI3bP8nAwhV78c19idB4O/dBpUREf25fYS3+83MWMgpqIZOIcEdCEBZPCoeXC0fI/RmloxwPJIdj0fhQbDtZiZXdow2/P1SGGH9XzE8MxrXDfDh66AL2FtbivV9zsSPbOFJ/apQXFk8K7zfrQEZ6OWPJFGc8elUEsiqbsOmIcY3DVelFWJVeBC8XO0wf4oMZw3wwItD6g8OqpnZsO1GFLScqkJZbg069cZTOMH9XTI3ywtRob0Soncw6ynMgEIlE+Nd1Q1Dd1IkNh8ugcrLDczMG8/tKA54gCMitasaObC12ZGuRUVCLzu7Rgr6uCowNckJUkBpB3TsLByodGCBZAZFIBLWLAmoXBRLDzh1k067To7CmO0CsakZ+tTFIPHq6Aen5Nefc104qRojnWSMRz5re7DTAlsfpDwoLC8+5vWDBAixYsAAAkJycfN6U47CwMGzYsKFvirsE/M0hkzt2ugF1rTrcGtf7XY6HB7rjo7kjcdeqvbjj0wysvz/xDxf9JiIyt6OlDXhjSxZ2ZGshEYswe5Q/HpoUwdemyyCViHH1EB9cPcQHJ8sbsTq9EN8ePI0lXx/Gy5tP4ra4QNyeEDTgA9gzI1jf/TUHGQW1EImAmTG+eHBiGAZ5u1i6vAsSiUQY5O2CQd4uWDIlEifLm7DpaBk2HinHyrRCrEwrhLeLAtcM9cGMGB8MD3CzmuAnT9uMLccrseVEBQ6V1EMQAKlYhDFhHpga5YWrorzg4/rHa1vRpZOIRXhrTizmfZqJ5bsL4O1qh0VJYZYui6jPNbbrkJZbbQwIs7Qo617mQC4VIz5EiQmRKiRrVAhTOZ23mSRZP4VM0vP39WyCIKCqqcM4AvHMSMTufzcfKz9vNKKXi13PNOYzYWKYygl+bvZWfyGPzIthIZnczp71ClWX9LikSBXevCUWD315EHd8moFv7kuEypnT+4iob2VVNGHp1iz8fLwSIhFwXYwvHrkqAqEqpz9/MP2pwT4ueOUvw/DE1YPw9b4SrE4vwju/5uKDlDxcPcQbC8cGY0Sgu9UESqYgCAJ+OVmF97bn4lBJPSRiEW4a6Y/7k8MQZkW/dyKRCFG+LojydcHfpmpwvKwRG4+UY9PRMizfXYDluwvg52aPa4YapyrH+Lv2q5+zwSDgUGl9T0CYr20BADjKJbhmqA+mRnkhWaMe8OuT9gWFTIKP543CzcvS8PLmU1A52+HG4f6WLmvAyCyoxTtbT0NzqhMjAt0xIsiNwXgfMBgEnChv7AkH9xfX9WyOEerpiAWJwZigUSEhxAP2co7IH6hEIhG8XBTwushoxILqlu4pzc3dU5pbcKS0/k9HI549vZmjEQlgWEhmkJpdDUe5BCMuYz2qGcN8Udeqw3PfHcOCFZn4clECXLiQKxH1gYLqFry1LRs/HC6DIBinfS6ZGtlvR3RZOzcHORYlheGucaH45WQlVqUXdq+BV44hfi6YPyYYM2N8bXpNJb1BwE/HKvDe9lycLG+EXCLG3IRA3JsUZvUjWEUiEYb4uWKInyueuFqDo6cbeqYqf7yzAB/vNAaHM7o3xRnqZ5ngsKNLj7S8Gmw5XoltJyuhbeoAAKic7XBbfCCmRnlhTJgHp8pbgKuDDKvujMNfPkjD498cgYej3SVfiKZLt3ZvMZ797hh0egG7iprxKQoAGDfDGBHojuGBbhgR5I5oXxf+vzCBmuYO7Mqtxo4s49qD1c2dAAAHuQQTNWpM0KgwIUKFQA/r/ptAfUMhk2CwjwsG+1x8NGJeT5Bo/PdioxHP3lwlTO2EUE9HjkYcYBgWkkk1tetwoLgOyRoV5FLxZbVxR0IQ6lo6sXRrNu5ZtQ+r7oyz6TeLRGRZpXWtePeXXKw7UAq9QcCESBUemxrZb9aGs3USsQhTo70xNdob2ZVNWJVWiP87cBqPrzuCV348hTmjAzA3IQi+brYzqqVLb8APh8vw/vZc5GlboJCJcde4ECxKCrXJqdgikQjD/N0wzN8NT04fhMOlDdh4uAybj5ZjWWo+lqXmI1DpYNxNe6gPon1dzBocNrTpkJJVhS3HK5GSVYWWTj0AIEzliJtG+mNqlBdi/N34hqgf8HG1x+o7jRvh3bdmP75alMDXZjPp0hvw8uZTWL67AP7u9vhHshqhIcE4UFyHg8V1OFBUj83HyrHpaDkA41TYIb4u3SMP3TEi0B3errb3+mVqXXoDDpfWY0eWce3BI6cbeoKaQd7OmDXSH8mRaowMcr/s91JEv9fb0YjGKc3GIPFwST3S8s4djaiQiRHsYRyFGNazyYoTQlR/vCs0WSeGhWRS6Xk16DIIV3zl96FJ4aht6cTKtEI89OVBfHj7CEgl/INJRKZT1diO97bn4svMYuj0AuJClPjbVI1Fdpklo0gvZ7x041D8/epB+KZ7ivIHKXlYlpqPadFemD8mGHEhyn41dfVSdHTpsX7/aXy4IxcltW1wspPigeQw3DUuZMDsqi0SiRAb4IbYADc8fc1gHCypx6Yj5dh8tBwfpuThw5Q8BHucCQ59MdjH2SQ/77L6Nmw7WYktxyuxJ994riISAcMD3DA12htTorysasr3QBLh5YxP54/q2Qhv/f2JCPbkG1NTamzX4aEvDmJHthZxwUp8OHcE6ipKEK52QrjaCbNHBfTc73BJPQ4U1f8WIhbXA7uMow99XRUY3h0cjgh0Q7SvKwMvABUN7Ujt3phkZ44Wjd07qLvay3DtUB9MiFQhKVJlkxeLqP/709GIVc3Iq/5tk5WLjUa8LUaJl7lupk1hWEgmlXpmvcKIKwsLRSIR/jEjCnWtnfj+UBme/vYoXps1zGrfIBJR/1Hb0omPduRhVVohOroMiAlww+NTNRgb7sHXmH7C1V6Gu8eH4s6xIUjJrsKK3YXYfLQCm49WYLCPCxYkBuH6WD+rGXXe1qnHV3uLsWxHPioa2+HmIMOSKZGYPyYYrg4Dd6kNsViEkUHuGBnkjmevHYwDxXXY2B0cvr89D+9vz0Oop6MxOBzmA41X74NDQRCQXdmMLccrsOVEJY6ebgAAyCVijI/wxNRob0werIbamW/OrcGoYCXeuXU47l+zH/NXZGId17U2mcLqFty1ai/ytC24ZVQAXrhhCORSMeoucF8XhQzjI1QY332ebzAIyK9u7gkPDxTXYfPRcmw68tvow6F+rhgR6NYzAnEgBGIdXXrsK6zDjmwtUrO1OFXRBAAQiYAYfzdMiFRhgkaFGH837lZM/dY5oxHDLzwa8czmKp9nFOHH7Aa8aBA4Kt+GMCwkk0rNrkaA0h5BJlhXQywW4Y2bY9DQpsPX+0rh7ijHU9MHm6BKIhqIGtp0+HRnPj7dVYCWTj0GeTvjb1M1mDxYzZCwnxKLRZg0yAuTBnkhT9uM1WmFWLe/FE+sP4pXfjyFW0YH4I6EIPi798+1nJo7urBmTxE+2ZmP6uZOeDrJ8dT0Qbg9IYiLh/+OWCzCqGAlRgUr8Y8ZUdhXVIeNR8qw+WgF3v01F+/+moswlSOuHeaLGcN8EOnlfF4beoOA/UV1PQFhcW0rAMBZIcUNsb6YGu2NpEgVv/dWalq0N164YQie+fYY7ly5F18uSuDP8gql5Vbj/s8PoKldh+dmROHOscGX9PdQLBYhXO2McLUzZo/+bfThoeIzIw/rcbC4DvuL6oDutQ/93OyN6x52h4dRPi42MfqwqKalZ2OStLwatOmMyxt4Otlh1gh/TNCoMC7cE0pHuYUrJbpyvx+N2NzRhf+l5uNEeSOG+LlauDrLio2NBQB0dnYiKysLQ4cOBQBoNBqsXbu21+3cddddSE9Ph0ajwbfffnvO7YULF2L79u148803zdGFHvwLSyZTVNOC4tpW3B4faLI33jKJGB/cPgJzP8nAsh35UDrIce+EMJO0TUQDQ0tHF1amFeJ/qfloaNMhVOWIJVMicc0QH179tCJhKif86/oh+Ns0DdbtL8Xq9CIs25GPj1PzcdVgLywYG4wxof1jdGhDqw4r0gqwYnchGtp08HFV4F/XReOW0QFWMxrSksRiEeJClIgLUeKfM6ORWVCLTUfL8OPRCrzzSw7e+SUHkV5OuHaoL6YN8UJmUTOWHTyMX05VobbFuDmAj6sC88cEYWq0N+JClJBxKRObcHt8EKoaO/D2Lzm4f81+fDp/tE0ETZbw2Z4iPP/DcTjIJPh0wWhM1KhN0q6LQoak7mm1wIVHH246atzsCDDuyDrUz7V73UNjiKi2gtGHrZ1d2JNf07P2YGGN8eKEtHvE9ASNChMiVRjs7cJzDbJ5yZEq/C81HylZVQM+LDx06BAAoLCwELGxsT23z9bV1QWp9OJRXGVlJb766is0NjZCIpGcdxsArrvuOnOUfw6GhWQyqdndU5BNvFOdg1yK5QtG45Zle/DKj6fg7ijvWTuFTKugugU/nKhDsrwOQ/1c+eaKrFq7To/PM4rxYUouqps74e9uj+dmROGGWF+ugWrFnBUyLBwbgvljgpGao8XKtEJsOVGJLScqofFyxrzEINw43A8O8r4/xalu7sCnuwrwWXoRmju6EKh0wFPTB+EvI/wZaFwmiViEMWEeGBPmgee7g8ONR8vx07EKvLktG29uy+657yBvZ8yND8TUaG+zb5JClvPIVRGoamrHl5kl+Pu6w1g6O5ZhzCXo0hvw740nsDq9CEEeDvh0/iiEq88fqWsqFxp92NDWvfZh95qHB4vrsK/ot4nP/XH0oSAIyKlq7gkHMwtq0ak39NR7W3wgJkSqkBjmAWfFwF1eggamUcFK2MtESMnSYvGkCEuX0y8FBwfjlltuwfbt2xEREYH//ve/uPXWW9HY2Ij29nZMnDgR77zzDhobGzFx4kS0t7dj5MiRmDNnDlavXn3ObW9vb3z33Xf47rvvAAArVqzA22+/DUEQIJPJsG7dOgQHB19xzQwLyWRSc6p7TupNzc1BjtV3xWHWh2l4cv0RuNrLMC3a2+TPM1AJgoA1GcV4adMJtOsMeCetCg5yCUYGuSMh1AMJoUoM9XOz+IkaUW90dhnw9b4SvPdrLioa2+HlYocXbxiC2aMC+DtsQ8RiEZI1aiRr1CiobsHq9EKs21eKZ749htd6pigHI9AEy2L8mYqGdvwvNR9fZBahXWdAuNoJD04Mw8xhDKZNSSoRIzHcE4nhnvj3ddHYk1+LX05Vwq6rBbclDemTnzVZnkgkwgvXD4G2qRPfHSqDl4sCT13DZWp6o6FVhwe/OIBdudUYE+qBD24fAXcLTIt1tT9/9GGettkYHnaPQNx4xPKjDxvadEjLrTZOL87WoryhHYBxLcaEMA/j2oORKoSpHHlxggY0uVSM4b6O2FNch4ZWnUXXY37ol4dQ0lRisvYMggFikfFcLsA5AO9Ofvey26qpqUFGRgZEIhHa29uxYcMGODk5Qa/X4/rrr8fXX3+NOXPmYPPmzeeMSpwzZ845t1euXNnTZkpKCv79738jLS0NPj4+aG1tvez6fo9hIZmETm9Ael4NRgS6wcVMV9O8XBT47K543PxRGh768iBWLYwzSzA50GibOvDE+iP49VQV/NzscUeMK9qkzsgoqEFGQS125lQDAOxlxvAwPkSJhDAPDPN3hZ2U0+mo/+jSG/DdoTK8/Us2Smrb4OEox3MzonB7fCCnftq4EE9H/HNmNB6bqsH/HSjFqrRCfLyzAJ/sKsDkQWrMTwzGuHBPk7+ZK6ltxYc78rBuXyk69QZE+bjgoUnhmBbtzZFOZiaViDEuwhPjIjyRm5vLoHCAkUrEePfW4Zj7aQaWpeZD5WyHu8eHWrqsfi1P24y7V+1DQXULbo8PxPPXRfebGSRisQgRXs6I8HLGLaMDARiDukMl9ThQZJy6fKi4/rzRh2eHh1G+LlfcH4NBwPGyRuzIrsKObC0OFNdDbzBu+RqqcsTCscGYEKlCfIgH7OU8ryA6W5y/I9KKmrEzV4sZw3wtXU6/tGDBgp5zUYPBgCeeeAK7du0y7jxdVYUhQ4Zgzpw5l9Tmpk2bcMcdd8DHxwcA4OBguvMhhoVkEgeK6tDc0dWzO5q5hHg6YuXCONz6vz24Z/U+fLUoYcCvi3Altp6oxJPrj6CmpROzRvjj+euiUFlahPDube/bdXocLqnHnvxaZBTUYG9hLXblVgNbjVd5jeGhB+JDlYgNcGMgQxZhMAjYdLQcb27LRr62BS4KKR6fpsGCxGA4cvH7AcXJTop5Y4JxR0IQduVWY1VaIX45VYVtJ6sQrnbC/DFB+MsI/yv+vcjTNuOD7Xn47tBp6A0CYgPc8PDkcEzUcLMcor5iL5fg0/mjcNNH6Xhx00moXRS4LoZvUC8kNVuLB784gNZOPf59fTTuSAjq969VrvaynpF7gPFvfa62uSc8PFBcjw2Hy7DhcBkA43npMH9XjAh0x/BAd4wIcuvVbuc1zR3YmVPds3NxTfe6p45yCSYNUvfUEKDkBQmiPzI6wBEAkJJl2bDwSkb+XUh7ezsUCtOMZHZycur5fOnSpaiqqkJGRgYUCgWWLFmC9vZ2kzyPqfBdFJnEmdFnpl6v8EKG+Lni4/mjMG95JhasyMQ39yUixNPR7M9rS1o6uvDiphP4MrMErvYyfHD7CFwz1Hg1ovKs+ylkEsSHeiA+1ANABDq69DhS2oCM/Brsya/F/qI6pOXVAOgefh7ghvjuacsjAt0ZHpJZCYKAbSer8N8tWThV0QRHuQQPTwrHXeND4WrP9YIGMpFIhPERKoyPUKG4phWr0wuxdl8Jnvv+OF7/KQs3jfLH/DHBCL7Evx0nyxvx/vZcbDpaDkEAEkKVeGhSBBLD+sfGKkQDjZuDHKvujMNfPtiNx74+BA9HOcaGe1q6rH5DEASsTCvECxtPwMlOipULR5v9wr65iMUiRHo5I9LLGXPiukcftupwsOS3dQ8PFddjb+Fvow/93e2N6x4GumFEkDsG+7hAbxCwr7C2Z2rx0dMNEIyDBzHYxwU3jwrAhEgVRga5c+kSokvg5SRDpJcTUrK0MBgEzrD4E3V1dfD29oZCoUBFRQW++eYbzJo165LbmTlzJhYsWID777//nGnIphhhyLCQTCI1Rws3BxmG9tEov4RQD7x763Dcv2Y/7vg0A+vvT4SXFeyc1h8cKK7DkrWHUFjTivERnvjPTTHwdu3d985OKsHoYCVGByuxeJJxbbijpxuwJ984ZXlfYS0yCmrxzi+AXCJGbIAb4kOViA/xwMggd07ZIJMQBAG7cqvxxpZsHC6ph0Imxr1Jobh3QhiUFlh7ifq3QA8HPDsjCkumRuLbg6exKq0QK3YbPyZqVJifGIykCNUfntQeKqnHe7/mYttJ4+WUZI0KiyeGY1Swsq+6QUQX4edmj1V3xuHmj9Jx72f7OeukW2eXAf/84Ti+zCxGqKcjPpk/CqEqpz9/oBVxdZD1rF0LAHqDgNyqM2sfGkcg/nC4DD90jz5UyMSQiICWTuPGSK72Mlw71AcTutdP5HsJoiuTrFHjf6n5OFHeyNfhP/HXv/4VN910E6Kjo+Hr64urrrrqstpJSkrCP//5T0ybNg0ikQhyuRzr1q1DUFDQFdfIsJCuWG1LJ46ebsA1Q30g6cMrCNOivfHqrGH4+7ojmPdpJr6+d4xFF1Pt73R6A977NRfvbc+FRCzCP2ZEYUFi8BVd9ZF3T0UeGeSOBycan+PY6QZkFNRiT34N9hXWIbOwFu8iFzKJCMP83ZBwVnjIKaJ0qfYW1uI/P2chs6AWMokI88cE4cGJ4X2y0DlZNwe5FLfHB+G2uECk59VgZVohtp2sxPYsLUI8HTF/TBBmjfQ/ZxfLjPwavLc9t2f0/LRoLyyeGIGh/jwBJupPBnm74ON5Z2ad7MW3DyQO6GmjdS2duG/NfmQU1GJ8hCfeu3XEgDhHlohF0Hg7Q+PtjFu7Rx/Wt3biYEk9DhYZRyDWNbXgqiH+mKBRIcbfrU/fuxDZuuRIFf6Xmo+UrKoBHxYGBwejvr6+53ZhYeE5xwMDA5GZmdmrx/7+9oIFC7BgwYKe2/Pnz8f8+fNNUPW5+E6drtiu3GoIAjDBAtMaZo8KQF1LJ1758RTuXLUXn90VBwc5f61/r6C6BY+sPYTDJfUY7OOCt+fEItLL2eTPI5OIMbx7rZj7JoShS2/A8bJG42Yp+bXILDBOXX5/ex6kYhGG+rsiPsQ4bXlUsBJODA/pIo6U1uONLdlIzdZCIhbhllEBeGhyOPzdB+6bQbo8IpGoZ0fdktpWrMkowleZJXh+wwn85+cs3DTSH4H2Ojy9LR2ZhbUQi4DrYnzx4MRwaLxN/7pJRKaREOqBt2+JxQNfHMC85ZlYd98YeDjZWbqsPpdT2YS7Vu1DcW0rFiQG49lrBw/oXdndHOSYqFFjYvfow9zc3J61uYnItEYFK+EolyAlS4vFkyIsXQ5dIb4zpyuWmq0FAIyPtMwaMfdOCENtSyeWpebjgc8P4ON5o/rN7m6WJggCvswswQsbT6C9S497J4RiyZTIPtvFWCoRIybADTEBbliUFAa9QcDJ8kbs6V7zMLOgBgeL6/HRjjxIxCIM8XVBQqhxw5RRwUqz7axN1uNURSOWbsnGlhOVEImA62N98chVkVynlEwiQOmAp6YPxiOTI/H9odNYmVaIVelFAACpWITZo/xxf3I4f9+IrMT0oT7493XReO7747hz5V58cU/CgJrFsP1UFR768iDadXq8dOMQ3B5/5dPQiIh6Sy4VIzHcE7+crERDq25AjGi2ZQPnryeZhSAI2JmjRYTaCT6u9har48npg1DX2omv95Xib98cxpuzYwf8oqrVzR14cv0RbDtZBT83e/x3dgwSQj0sWpNELMIQP1cM8XPF3eNDoTcIOFXRiIx847TlzMJaHC5twLLUfIhFQLSvK+JDlEgI9cDoEOWA37SiS29AfZsO9a2dqG0x/lte3oQiXSUkYhFkEjEkYhGkYhGkEjGkYtGFb0tEkIrPvq/xtliEfrNJQ762GW9ty8GGI2UQBODqaG88OiWSI7vILOzlEsyJC8QtowOQUVCLXw7lYf7EIRy5SmSF7hgTjMrGDry3PRcPfH4An8y3/YvIgiDgk50FePnHk3C1l2H1vDgkhnGjFyLqe8kaFbaeqMTOXMvuikxXjmEhXZHsymZUNnZY/IVAJBLh5RuHor5Vh+8PlcHdQY5/zozqN8FHX/vlZCWeWH8E1c2duCHWF/+6fki/DNokYhGifV0R7euKO8eFwGAQkF3VhD15xg1TMgpq8cmuAnyyqwAiERDl44L4EI/uTVOUcHOw3s0suvQGNLTpUNcd/NW2dBpDwNZO1LV0oq5Vh7oW4+36VuPxhjbdRVorM1ldMsmZgFHcHUD+dlsq+S18lIjFZx0TnR9WisWQSM46dtbt37f1+9t7s8uxLS8beoOAZI0Kj03RcI046hMikQgJoR7wNNQxKCSyYo9NjURVUzu+3leKJ9YfwX9vjrHZc8KOLj2e/fYYvtlfinC1Ez6dPwpBHhwNTUSWcWbDoZQs84eFZ17XhTNbmtMFnfn+XOrfQYaFdEXOTEFOiuz79Qp/TyoR451bh2P+8kysTCuE0lGOhycPrLUSWju78OKmk/gioxguCineuXU4rouxnis6YrEIg7xdMMjbBQvGGsPDXG2zcbfl/FpkFNRg+e4CLN9tDA81Xs5ICDWueRgX4mGxnXB/H/zVdQd+lxf8/UYuEcPdUQZ3BzmifFygdJTDzUEGpaMc7g7Gz7VVVfBQqdGlN6DLIEBvEKDTG6A3CD23zz0mQG+48O0u/ZnH/Hbb2M75tzu6us47ru9+fJfBAJ3+yv5ox4co8bdpGozmbrNERHSJzlxErm7uxP8dOA0vFwWeuHqQpcsyuermDty/Zj/2FtYhWaPCO7cO5xIuRGRRfm72iFA7YUe2FgaDYNbZfmKxGAqFAqdPn4aXlxdkMtO//hkMBuj1epO321cEQUBNTQ1kMhnE4ksbZc+wkK5Iao4WcqkYcf3kDb1CJsHH80fh1v/twdKt2XB3lOOOhIGxXsvhkno8svYQCqpbkBjmgTdujoGvm+WmhpuCWCxCpJczIr2cMW9MMARBQJ62GXvya3t2XF6ZVoiVaYUAjOFhfKhx2nJciBKel7Gwud4goL6184LB35mgzxzB35ljv92Ww1Eu+dMrQLm57QgP97/kfvYFg0GAznBWcKk/6/YFgskzt2sqyjAlLtpmR4EQEZH5SSVivHfbcNz2cQY+TMmD2tkOC8eGWLoskzlV0Yi7Vu7D6fo23D0uBE9dM5g7+xJRv5CsUeHjnQU4Ud5o9l2Rg4KCUFVVhcLCQrOMMNTpdGYJIfuSTCZDYGDgJT+OYSFdtnadHhkFtYgPUcJe3jcbZvSGi0KGlQvjcPNHafjH98fg7iCz+DRpc+rSG/BBSh7e/iUHEpEIz147GHeODbHJNRtFIhHC1c4IVztjbkIQBEFAQXVLd3hYgz35NVidXoTV3RsUhKudkBCqRHyIB7qaWpHXUXHB4K+u1Tj6r7alE43tOvzZ3xmZRNQT7EX5uFwg6Lu84M/WiMUi2Ikv/bUhV1c74L5XRERkeg5yKZYvGI2bPkzDvzeegMrZzibOCbeeqMQjXx1Ep96A12cNw+zRAZYuiYioR7JGjY93FmBHttbsYaFYLIa3tze8vLwgCILJA8P8/HyEhoaatM2+JBKJLnlE4RkMC+myZRTUorPLgKQIy09B/j2Vsx0+uyseN32UhkfXHoKLQtYvpkqbWlFNCx5dewgHiusxyNsZb82JxSBvF0uX1WdEIhFCVU4IVTnhtvhACIKAoprW7uCwFhn5NVizpxhr9hR3P6LkvDb+KPhzc5BDyeCPiIjIaikd5Vh1Zxz+8mEalqw9DKWj3Go3/xAEAR/uyMN/fs6Cu4McKxbGIS6kf8zuISI6Y1SwOxzkEqRkVeHBieF98pwikcgs789EIhEkkv4zMKovMSyky3ZmvcLxkf3zhCtA6YDVd8Zj9rJ03LdmPz6/Ox7DA90tXZZJCIKAb/aV4l8bjqOlU497xofgsakaKGQD84XsDJFIhGBPRwR7OuKW0cbwsLSuDXvya5BfWo7IIF9j4Hcm/GPwR0REZPMClA5YtTAOtyxLx72r92PtvWMQ5WtdF1fbdXo89X9H8e3B09B4OeOT+aMQoORGTETU/9hJJUgM88SvpyrR0KqDq4N1T+MdqC5vPCIRgJ05Wni52EHj5WzpUi5K4+2M5QtGwSAIWLhyL3Iqmyxd0hWrbenEvZ/tx9/XH4GLvQxf3B2PZ66NGvBB4YWIRCIEKB1w86gAzBqixI3D/ZGsUSMmwA0BSgc42UkZFBIREQ0AUb4uWDZvJDq6DFiwIhMlta2WLqnXqpracevHe/DtwdO4arAa6x9IZFBIRP3axEEqGARgZ67W0qXQZWJYSJelvKEN2ZXNGB+h6vdhy8ggJT6cOxLN7V2YtzwTp+vbLF3SZdueVYVpb6Viy4lKzIzxxU9/TUJieP8c2UlERETUnySGeWLpLTHQNndg/opM1LZ0WrqkP3XsdAOuf283DhbX4/7kMCy7YxSc7Dg5jIj6t2SNGgCQksWw0FoxLKTLsjO7GgAwPsI6gqqJGjX+OzsG5Q3tuOPTDNQ0d1i6pEvS1qnHP74/hoUr9qJdp8fbc2Lx7q3DOaSbiIiI6BLMGOaLf86IQr62BXeu3IvWzi5Ll3RRPx4tx80fpaOmuRNLZ8fgiasHccdjIrIKfm72iFA7YUe2FgaD6XcpJvNjWEiXJTVHC5EIGN8PNze5mOtj/fD8TOPJ4cKVe9Hc0X9PDs92tLQBM97didXpRYgPUeKnR5JwfayfpcsiIiIiskoLxobg/uQwHCqpx+IvDqJLb7B0SecQBAHv/JKD+z8/AEc7Cb5clIC/jPC3dFlERJckWaOCtqkDJ8obLV0KXQaGhXTJ9AYBu3KrMdTPFUpHuaXLuSQLxobg4ckROFLagEWr96GjS2/pki5KbxDw/vZc3PjBbhTXtuKp6YPwxT0J8HOzt3RpRERERFbt79M0+MsIP/x6qgpPf3sUgtA/Rr606/R4+KtDWLo1G1E+Lvh+8TiMDLKNDfqIaGA5MxV5RzanIlsjhoV0yY6ebkB9q85qpiD/3qNXReCOhCCk5dXgka8OQd8Ph0WX1LbilmXp+M/PWQhVOeK7B8fi3glhnHpCREREZAIikQivzRqGCZEqfL2vFEu3Zlu6JFQ2tmP2snRsOFyGadFeWHf/GF4kJiKrNSrYHQ5yCVKyqixdCl0GhoV0yXZ2XxlIsqIpyGcTiUR4/rpozBjmgx+PVeDZ7/rP1WRBELBufymmv70T+4rqsHBsMH5YPA7Rvq6WLo2IiIjIpsgkYnxw+wjE+Lvi3V9z8Vl6ocVqOVxSj+ve24UjpQ14eFI4Prx9JBzk3MiEiKyXnVSCxDBPHCiuR0ObztLl0CViWEiXLDVHC0e5BCOseEqERCzC0tmxGB/hiS8zS/DGlixLl4S6lk488PkB/O2bw3C0k+Czu+Lwz5nRUMgkli6NiIiIyCY52kmxfMFoBHs44B8/HMdPx8r7vIYNh8swe1k66lt1eOfW4VgyVQMxZ5MQkQ1I1qiMy5jlVFu6FLpEDAvpkjS263CguB5jwjwhk1j3r49cKsZHc0ciNsAN72/Pwyc78y1WS2q2FtPeSsWPxypw7VAf/PxIklVtHkNERERkrTyc7LD6znh4ONrh4a8OISO/pk+e12AQsHRLFh768iBc7WX4+t4xuC7Gt0+em4ioLyRrjO9pORXZ+lh32kN9Lj2vBnqDgAmR1rle4e852kmxYsFohKud8OKmk1i/v7RPn79dp8fzPxzHvOWZaO3UY+nsGLx323C4OVjXxjFERERE1izQwwErF46GXCLG3av34VSFeXfvbO3swoNfHMA7v+ZiqJ8rflg8DjEBbmZ9TiKivubv7oBwtRN2ZGv7zdJf1DsMC+mSpJ5ZrzDSdka9uTvK8dldcfBzs8ff1x/BLycr++R5j51uwMx3d2FlWiHigpX48a/j8ZcR/hCJOO2EiIiIqK8N8XPFR3NHol2nx4Lle3G6vs0sz1NW34abP0o3zigZ5oOv7x0Db1eFWZ6LiMjSkiNVqGrqwIly816EIdNiWEi9JggCUnO0CFQ6IMjD0dLlmJSPqz1W3xUHV3sZHvj8ADILas32XHqDgA9T8nDjB7tRWNOCv1+twZeLEhCgdDDbcxIRERHRnxsX4Yk3bo5BRWM75i/PRH1rp0nbP1Bch+ve243jZY1YMiUS7906HPZyrk9NRLZr4iA1ACAlS2vhSuhSMCykXiuqaUVJbRuSbGQK8u+FqZywcuFoSMUi3LVqL06Umf7KR2ldK279eA9e++kUgjwc8e0DY/FAcjgkXMSaiIiIqF+4PtYPz147GLlVzbhr1T606/Qmaffbg6WY8789aO7Q4YPbR+DhyRGcUUJENm9UsDsc5BKuW2hlGBZSr6XmdE9BtuGNN4b5u+F/80ahQ2fA/BWZKK5pNUm7giDg24OlmP7WTmQW1GJBYjA2PjQOQ/xcTdI+EREREZnO3eNDsSgpFPuL6rD4i4Po0hsuuy2DQcBrP53Co2sPw8NRjnX3JeKaoT4mrJaIqP+yk0qQGOaJA8X1aGjTWboc6iWGhdRrqdlaSMUijAnzsHQpZjU23BNvz4lFTXMH5n6agaqm9itqr6FVh4e+PIhH1x6GQi7Bqjvj8Px10VDIOOWEiIiIqL968upBuCHWF9tOVuK5749f1uL8zR1dWPTZfnyYkofYADd8/+BYXiwmogEnWaOC3iBgV061pUuhXmJYSL3S2WVAel4NRgS6w1khs3Q5Zjd9qA9eunEoimtbMe/TzMu+ArI7txrT3krFxiPluDraGz8/koQJNrQ5DBEREZGtEotFeP2mGIyP8MSXmcV4a1vOJT2+pLYVN32Yhm0nK3FDrC++WpQAtQs3MiGigSdZY3wPzKnI1oNhIfXKgeI6tHTqMT7CNtcrvJBb4wLx+DQNTlU04Z5LXK+mXafHCxtP4PZPMtDUrsPrNw3Dh3NHQOkoN2PFRERERGRKcqkYH84diSF+Lnj7lxx8nlHUq8ftLazFDe/vxqmKJjw+TYM3b4nlrBIiGrD83R0QrnbCjmztZY3Spr7HsJB6JTW7e73CATYq7oHkMNw1LgSZhbVY/MUB6HqxXs3J8kZc/95ufLqrACOD3PHjX5Mwe1QAF7AmIiIiskJOdlKsWBCHQKUDnvvuGH4+XvGH9/9mXwlu+3gP2nR6/O+OkXhwYjjPA4lowEuOVKGqqQMnyk2/kSiZHsNC6pWdOdVwd5ANuDVWRCIRnrlmMP4y3A/bTlbhifVHYDBc+EqIwSDgf6l5uP693cjTNuNvUyOxdlECAj0c+rhqIiIiIjIllbMdVt8ZB3cHOR7+8iD2Ftaedx+9QcBLm07g8XVHoHZWYN19iZga7W2BaomI+p9kjRoAkJKltXAl1BsMC+lP1TR34FhZA8aGe0IiHnhXRcViEV67aRgmD1Lj/w6cxsubT543dLqsvg23f5KBlzefgr+7Pf7vgUQsnhQBqYT/xYiIiIhsQbCnI1YsHA2JWIS7Vu5FdmVTz7Gmdh3uXrUXH+8swKggd3y/eCyifF0sWC0RUf8yOsQdDnIJdjAstApMMuhP7cqthiAMvCnIZ5NJxHj/9hEYHeyOT3YV4MMdeT3Hvj90GtPeSkV6fg3mJgRi48PjMMzfzXLFEhEREZFZDPN3w4dzR6K1U4/5yzNR3tCGssZO/OWDNGzP0uKmkf74/J54eDrZWbpUIqJ+xU4qQWKYB/YX1132BqLUd6SWLoD6v9Rs4/bmSREDNywEAIVMgk/mj8Yty9Lx+k9ZkEvESDtVhl/zmuDpZId3FgzHxEFqS5dJRERERGY0IVKF128ahiVfH8btH2dA29SG5k4DnrlmMO4eH8L1CYmILmKCRo1tJ6uwO7ca1wz1sXQ59Ac4spD+kCAI2JmjRaSXE7xdFZYux+Jc7WVYfZdxgesXN53Er3lNmBLlhZ8fGc+gkIiIiGiA+MsIfzw1fRDyq1ugNwCfzh+Fe5JCGRQSEf2B5O7ZittPVVm4EvozHFlIfyirsglVTR24LsbX0qX0G2pnBdbcFY9nvz+G0V5iLL5mJE8MiYiIiAaYRUmhCPZ0hF17LZIHeVm6HCKifi9A6YBwtRN2ZGshCALfR/djHFlIfyg127j46EBer/BCAj0csPrOOEzXuPEFjoiIiGgAEolEmBbtDX9XuaVLISKyGsmRKlQ1deBEeaOlS6E/wLCQ/lBqdjXspGLEhSgtXQoRERERERERWbFkjXH5rhTuityvMSyki2rr1COzsBZxIUooZBJLl0NEREREREREVmx0iDsc5BLsYFjYrzEspIvKKKhBZ5cBEzgFmYiIiIiIiIiukJ1UgsQwD+wvrkNDm87S5dBFMCyki0rNrgYAjI9gWEhEREREREREV26CRg29QcDu3GpLl0IXwbCQLmpnjhbeLgpEejlZuhQiIiIiIiIisgHJ3bMXU7KqLFwJXQzDQrqgsvo25FQ1Y3yEJ3f7JSIiIiIiIiKTCFA6IEzliB3ZWgiCYOly6AIYFtIF7cwxLjY6nusVEhEREREREZEJJWvUqGzswMnyJkuXQhfAsJAuKDWnGiIRMD7c09KlEBEREREREZENSdZ0T0XO5lTk/ohhIZ1HbxCwK6caw/xc4e4ot3Q5RERERERERGRD4kKUsJdJkHJKa+lS6AIYFtJ5jpTWo6FNx12QiYiIiIiIiMjk7KQSjA33wP7iOjS06SxdDv0Ow0I6z84c4/blSVyvkIiIiIiIiIjMYIJGDb1BwO7cakuXQr/DsJDOk5qthZOdFMMD3SxdChERERERERHZoOTuAUopWVy3sL9hWEjnaGzX4WBJPcaEeUAm4a8HEREREREREZlegNIBYSpH7MjWQhAES5dDZ2EaROdIy62B3iBwCjIRERERERERmVWyRo3Kxg6cLG+ydCl0FoaFdI7UHONORBO4uQkRERERERERmVGypnsqcjanIvcnDAuphyAISM3WIsjDAYEeDpYuh4iIiIiIiIhsWFyIEvYyCVKytJYuhc7CsJB6FNa0orSuDUkcVUhEREREREREZmYnlSAxzAP7i+rQ2K6zdDnUjWEh9UjNNib5XK+QiIiIiIiIiPpCskYFvUHA7pxqS5dC3RgWUo/UbC2kYhESQpWWLoWIiIiIiIiIBoBkjRoAOBW5H2FYSACAzi4D0vNrMCLIHc4KmaXLISIiIiIiIqIBIEDpgFCVI1KyqyAIgqXLITAspG77i+rQ2qlHUoSnpUshIiIiIiIiogFkokaNysYOnCxvsnQpBIaF1C01h+sVEhEREREREVHfS9YYs4iU7CoLV0IAw0LqtjNHC3cHGYb4upq87eLGYlR3cKFSIiIiIiIiIjpfXIgS9jIJ1y3sJ6SWLoAsr7q5A8dON2JmjC/EYpFJ2zYIBiz8aSEEg4BNmk2wl9qbtH0iIiIiIiIism52UgkSwzyQkq1FY7sOLtxLwaI4spCwq3t7cnOsV5hVm4WqtipoO7RYcWyFydsnIiIiIiIiIuuXrFFBbxCwO4czEy2NYSGZdb3CtLI0AICDxAHLjy3H6ebTJn8OIiIiIiIiIrJuyRo1AHAqcj/AsHCAEwQBO3OqofFyhpeLwuTtp5enw05ih0cjH0WHvgP/2fsfkz8HEREREREREVm3AKUDQlWO2JGthSAIli5nQGNYOMCdLG+CtqkDSZGmn4Lc1tWGg5UHMUI9AqOUozAlaAp+Kf6lZ7QhEREREREREdEZyZFqVDS241RFk6VLGdAYFg5wO804BflA5QF0GjoxxncMAODxUY9DIVHg1cxXodPrTP58RERERERERGS9kjXGbIJTkS2LYeEAl5qjhZ1UjNHBSpO3nV6WDgBI9E0EAPg4+eCuoXehoKEAX5z6wuTPR0RERERERETWKy5ECXuZBClZVZYuZUBjWDiAtXZ2YW9BHeJDPaCQSUzefnp5OpQKJSLcI3q+tnDIQvg5+eHDwx9C28orBURERERERERkpJBJMCbMA/uK6tDYzhmJlsKwcADLKKhFp96ApAjTr1dY3VaN7LpsJPgkQCz67dfMTmKHv4/+O1p0LXjrwFsmf14iIiIiIiIisl7JGhX0BgG7c6otXcqA1euwMDg4GBqNBrGxsYiNjcXatWsBADk5OUhMTERkZCRGjx6N48eP9zzmco9R30jNNt96hb+fgny2iQETMdZ3LH7I+wGHqg6Z/LmJiIiIiIiIyDolR6oBcN1CS7qkkYVr167FoUOHcOjQIdxyyy0AgHvvvReLFi1CdnY2nnjiCSxYsKDn/pd7jPpGarYW3i4KRKidTN72nvI9AIAEn4TzjolEIvw97u+QiqR4JfMV6A16kz8/EREREREREVmfQA8HhKocsSNbC0EQLF3OgHRF05Crqqqwb98+zJ07FwAwa9YslJSUIDc397KPUd84Xd+GPG0LkiI9IRKJTNq2IAhIL0tHmGsYvBy9LnifUNdQzI2aixM1J/Bt7rcmfX4iIiIiIiIisl7JkWpUNLbjVEWTpUsZkKSXcud58+ZBEATExcXh1VdfRUlJCXx8fCCVGpsRiUQIDAxEcXExXF1dL+tYeHj4Oc+5dOlSLF26tOd2Y2OjTYeK1dV9Myd/86l6AECEs97k38+iliJo27RIcEvoaftC/ZriNAXfy77Hm3vfRLghHE5S049wNLe++nn1JVvsE8B+WRNb7BPAflkTW+wTwH5ZE1vsE8B+WRNb7BPAflkbW+yXLfYJME+/Ip2Nm5usTzuJOTEeJm+/N2z159UbvQ4LU1NTERgYCJ1Oh2effRbz58/HCy+8YM7aAABLlizBkiVLem77+/ufFyjamr7oX1bGAYhEwE3jhsDdUW7SttOOpwEAro66GuH+v/XlQv16XPY4nt71NDY3bMbT8U+btI6+You/j7bYJ4D9sia22CeA/bImttgngP2yJrbYJ4D9sia22CeA/bI2ttgvW+wTYPp++Qfp8a9fynG02oBnLfg9s9Wf15/p9TTkwMBAAIBMJsMjjzyCnTt3IiAgAOXl5ejq6gJgnH5aXFyMwMDAyz5G5qc3CNiVW41h/m4mDwoBIK08DVKxFKO8Rv3pfWeEzkCsKhZrs9YiqzbL5LUQERERERERkXVRyCQYE+aB/UV1aGrXWbqcAadXYWFLSwvq6+t7bn/55ZcYPnw41Go1RowYgTVr1gAA1q9f3zPy73KPkfkdLq1HQ5sOSRGeJm+7U9+J/RX7MVw9HA4yhz+9v0gkwlPxT0EQBLyS+QoXLyUiIiIiIiIiJGtU6DII2J07cKcDW0qvpiFXVlZi1qxZ0Ov1EAQBoaGhWL16NQBg2bJlWLBgAV5++WW4uLhgxYoVPY+73GNkXjuzjf/RkiJVJm/7UNUhtOvbMcZnTK8fE+URhZsib8I32d/g58KfcXXI1Savi4iIiIiIiIisR3KkGsBxpGRpcfUQH0uXM6D0KiwMDQ3FwYMHL3hMo9EgPT3dpMfIvFJztHC2kyI2wM3kbaeVGdcrHOPb+7AQAB4a/hB+LvwZb+x7A0n+Sb0alUhEREREREREtinQwwGhno5IydJCEASIRCJLlzRg9HrNQrINDW06HCqpx5gwD8gkpv/xp5enw9XOFYOVgy/pce4KdywevhiVrZX45OgnJq+LiIiIiIiIiKzLBI0KFY3tyKpssnQpAwrDwgEmPa8aeoNglinIde11OFlzEvHe8ZCIJZf8+Jsjb0akeyRWHl+J4sZik9dHRERERERERNYjWaMGAGw/pbVwJQMLw8IBZkf3eoUTzBAWZpRnQIBwyVOQz5CKpXgq7inoDDq8vvd1E1dHRERERERERNYkPkQJhUyMlKwqS5cyoDAsHEAEQUBqthbBHg4IUJp+TcD0cuMalJcbFgLAKO9RmB4yHTtKdyC1NNVUpRERERERERGRlVHIJEgM88T+ojo0tessXc6AwbBwACmobsHp+jazTEEWBAHpZekIcgmCn5PfFbX12MjHYC+1x2uZr6FT32miComIiIiIiIjI2iRrVOgyCNidW23pUgYMhoUDSGq2cY7/+AjTh4WFjYUobynHGJ/LH1V4hpejFxYNW4TipmJ8duIzE1RHRERERERERNYoOdK4bmFKFtct7CsMCweQ1JxqSMUijAnzMHnb6WVXPgX5bPOi5iHQORDLjixDZUulSdokIiIiIiIiIusS6OGAUE9HpGRpIQiCpcsZEBgWDhAdXXqk59VgZJA7nOykJm8/vTwdEpEEo71Hm6Q9uUSOJ+KeQFtXG5buX2qSNomIiIiIiIjI+kzQqFDR2I6syiZLlzIgMCwcIPYX1aFNpzfLeoU6gw57K/ZimGoYnOXOJms3yT8JSf5J2FywGfsr95usXSIiIiIiIiKyHskaTkXuSwwLB4jUbONCoElmWK/wqPYoWnQtJlmv8PeeGP0EZGIZXsl4BXqD3uTtExEREREREVH/Fh+ihEImRkpWlaVLGRAYFg4QO3O0UDrKEe3rYvK208rSAJhuvcKzBboEYn70fGTVZeGb7G9M3j4RERERERER9W8KmQRjQj2wr7AOTe06S5dj8xgWDgDapg4cL2vEuHBPiMUik7efXp4OJ5kThngOMXnbAHDP0HugdlDj3YPvoq69zizPQURERERERET9V7JGjS6DgN25NZYuxeYxLBwAduUa5/SbY73Cxs5GHKs+hjjvOEjFpt84BQAcZA54bORjaOxsxHsH3zPLcxARERERERFR/5WsMWYanIpsfgwLB4CdPesVepq87czyTBgEg1mmIJ9tesh0jPQaiW+yv8HJmpNmfS4iIiIiIiIi6l+CPBwR4umIlCwtBEGwdDk2jWGhjTMYBKTmVGOQtzPULgqTt59elg4ASPRNNHnbZxOJRHgq7imIRCK8kvkKXxiIiIiIiIiIBphkjQoVje3IqmyydCk2jWGhjTtZ0Yjq5g6zTEEGjOsV+jn5IcA5wCztn02j1GB25GwcrDqIjfkbzf58RERERERERNR/JGvUAICULK2FK7FtDAtt3M6cM1OQTR8WljSVoKSpBAk+CRCJTL9xyoUsHr4YbnZueHP/m2jRtfTJcxIRERERERGR5cWHKKGQibluoZkxLLRxqdlaKGRijAp2N3nbfTUF+Wyudq54eMTD0LZpsezwsj57XiIiIiIiIiKyLIVMgjGhHthXWIemdp2ly7FZDAttWGtnF/YV1iE+xAMKmcTk7e8p3wMRRIj3iTd523/kL+F/wWDlYHx28jMUNBT06XMTERERERERkeUka9ToMgjYnVtj6VJsFsNCG5aRX4tOvcEs6xXqDXrsKd+DaI9ouNq5mrz9PyIRS/B0/NPoMnThtczXuNkJERERERER0QCRrDFmHDuyORXZXBgW2rAd2cYFP5MiPE3e9vGa42jqbMIY3zEmb7s3YtWxmBk6E7vLdiOlJMUiNRARERERERFR3wrycESIpyNSsrQcPGQmDAttWGqOFj6uCoSrnUze9pn1Ci0VFgLAoyMfhaPMEa/vfR0d+g6L1UFEREREREREfWdCpArlDe3Irmy2dCk2iWGhjSqta0W+tgVJESqz7FScVpYGe6k9YlWxJm+7t1QOKtw37D6UNpdi5bGVFquDiIiIiIiIiPrOmanI3BXZPBgW2qidOdUAgPGRpp+C3KJrwRHtEYz2Hg2ZRGby9i/F7YNvR7BLMD45+gnKm8stWgsRERERERERmV9CqAfspGJsZ1hoFgwLbVRqthZiETAu3PRh4b6KfegSujDGx3JTkM+QSWR4Ku4ptOvb8ca+NyxdDhERERERERGZmUImwZgwD+wrrENTu87S5dgchoU2qEtvwO7cagzzd4Obg9zk7aeVpQGw7HqFZ0v0S8TEgInYUrQFGeUZli6HiIiIiIiIiMxsokaNLoOA3bk1li7F5jAstEGHSxvQ2N5lll2QASC9PB1qBzVCXUPN0v7leHz045CL5Xg181XoDLyqQERERERERGTLzqxbuCObU5FNjWGhDUrN1gIAkiJVJm+7oqUCBQ0FGOMzxiwbp1yuAOcALByyELn1ufg662tLl0NEREREREREZhTk4YgQT0ekZGkhCIKly7EpDAtt0M4cLZztpIgNcDN52+ll6QCARN9Ek7d9pe4aehd8HH3w/sH3UdPGYchEREREREREtmxCpArlDe3Irmy2dCk2hWGhjWlo1eFQST0Swz0glZj+x3smLIz3iTd521fKXmqPv436G5p0TXjn4DuWLoeIiIiIiIiIzOjMVOQU7opsUgwLbczuvGoYBPNMQTYIBuwp34NBykHwsPcwefumMCVoCuK94/Ftzrc4Vn3M0uUQERERERERkZkkhHrATipGSpbW0qXYFIaFNmZnTvd6hRGmDwtP1Z5CXUddv9kF+UJEIhGejHsSYpEYL2e8DINgsHRJRERERERERGQGCpkEY8I8sK+oFs0dXZYux2YwLLQhgiAgNbsaIZ6OCFA6mLz9M1OQx/j037AQAMLdw3HroFtxtPoovs/93tLlEBEREREREZGZJEeqoNML2J1bbelSbAbDQhuSX92C0/VtSIrwNEv76eXpsJPYYYTXCLO0b0r3x94PpUKJtw68habOJkuXQ0RERERERERmkKxRAwCnIpsQw0Ibkppt/I8x3gxTkNu62nCg8gBGeo2EncTO5O2bmovcBY+MeAS17bX48PCHli6HiIiIiIiIiMwg2NMRwR4OSMmqgiAIli7HJkgtXQCZTmq2FjKJCGPCTL/5yIHKA9AZdP1+CvLZrg+/Ht9kf4MvT36JWRGzEOYWZumSiIiI+lyrrhXvHXoP+0r3wbvYG252bnCzc4OrnSvcFe5wtXPt+dqZr0vFPEUkIiIi65GsUWNlWiGyK5uh8Xa2dDlWj2eCNqKjS489+bUYGeQORzvT/1h71ivsx5ub/J5YJMZTcU/hts234ZXMV/DxlI8hEoksXRYREVGf2V+5H8/uehalzaVwkDgguykbekH/p49zljnDTfFbeHh2mOhm5wZXhSvc7dzPOa6QKvqgR0RERETnm6BRYWVaIVKyqhgWmgDDQhuxv7AObTq9WaYgA0BaeRo8FB6IcI8wS/vmMlQ1FDeG34hvc7/FtuJtmBI0xdIlERERmV2HvgPvHngXq0+shlwixxOjn0CcLA4R4RFo1jWjvr0e9R3nfzR0NKCuvQ4NHQ2o76hHVWsVsuuy0aHv+NPntJfa9wSHrnbGMPHM7QuNYHSzc4OjzJEX8oiIiOiKjQn1gJ1UjJQsLe6dwFmFV4phoY3YkWNcr3BCpOnDwuq2auTU5eDa0GshFlnfMpd/HfFXbC3aiv/s/Q/G+Y2DvdTe0iURERGZzfHq43h619PIb8jHMM9heHHciwhxDUFubi5EIhGc5c5wljsjAAG9brOtq60nSDwTKtZ31KOu47dgsb6jvieELG0qRbOu+U/blYqlcJVfeDr02aMWzz7uIneBRCy5km8RERER2RiFTIIxYR7YnVuN5o4uOJlhxuVAwu+ejdiZXQ0PRzmifFxM3nbPFGQrWq/wbB72Hngg9gG8vvd1LD+2HA/GPmjpkoiIiExOZ9Dhf0f+h4+PGJfdeHj4w1g4ZKFJ1h+0l9rDXmoPb0fv3tej16Ghs+GCoxh/P4KxvqMeefV5aOhogIA/XphcBBFc7Fx6AsUgWRBudrkZw1TDrPKiJhEREZlGcqQKKVla7M6txrTo3p+z0PkYFtoAbVMHTpQ34vpYX4jFpp/KY43rFf7enEFzsD57PZYfXY7rw66Hv7O/pUsiIiIymZy6HDyz6xmcrD2JSPdIvDzuZWiUGovWJJPI4GnvCU97z14/xiAY0NTZdN4IxnM+zgofixqLcLjjMH4o+wFqBzWmBE3BlKApiFXFcvQhERHRAJOsUQMbTiAlS8uw8AoxLLQBO7unICeZYb1CQRCwp3wPwt3CoXZQm7z9viITy/BU/FO4e8vdeGPfG3hr4luWLomIiOiK6Q16rD6xGu8efBd6QY97ht6D+2Puh0wis3Rpl0UsEsPVzhWudq69ur8gCPjp0E84aTiJrYVb8fnJz/H5yc/hae+JyYGTMTVoKkZ4jeDuzkRERANAsKcjgj0csCOrCoIgcF3kK8AzJxuwM6caADA+ovdX7nsrtz4X2jYtpgVPM3nbfS3eJx5TgqZga9FWpJWlIdE30dIlERERXbbixmI8u/tZHKw6iGCXYLw07iUMUw2zdFl9SiQSIcI5AtPDp+PREY/iVO0pbC3aii1FW7A2ay3WZq2FUqHEpMBJmBI0BaO9R0Mmts4glYiIiP5cskaNlWmFyKlqRqQXd0W+XFzYxcoZDAJ25mgxyNsZaheFydtPK0sDAJsJ1h4f9TgUEgVezXwVOr3O0uUQERFdMoNgwFenvsJNG27CwaqDmDt4Lr6e+fWACwp/TyQSYbDHYDw84mFsuGED1l+3HvfF3Ad3O3esy16He7fei4lfT8Q/dv8DO0t38jyAiIjIBk3QGGdcpmRVWbgS68aRhVbuRHkjqps7MWuEedbgSy9Ph0wsw0ivkWZpv6/5OPngrqF34f1D7+OLU19gfvR8S5dERETUaxUtFXhu93PYU74Hvo6+eG/Se4jzibN0Wf2OSCRCpHskIt0j8WDsg8irz8PWoq3YWrQV3+Z+i29zv4WzzBkTAydiStAUjPEdAzuJnaXLJiIiois0JtQDdlIxUrK0WJQUZulyrBbDQit3ZgpyUqTp1yvs1Hdif8V+xKpj4SBzMHn7lrIgegG+y/0OHx7+ENeEXAOVg+m/d0RERKYkCAJ+yPsBr2a+imZdM2ZFzMLfRv0NTnInS5dmFcLcwhDmFob7Yu5DYUMhthVvw5bCLfgh7wf8kPcDHGWOmOA/AVODpmKs31gopKafrUFERETmp5BJkBDqgbS8ajR3dMHJjrHX5eA0ZCuXmq2FQibGyCB3k7d9sOog2vXtNjMF+QyFVIHHRz+OFl0L3jrwlqXLISIi+kPVbdV4ePvDeHb3s7CX2uP9ye/j+cTnGRRepmDXYNw99G58PfNrbP7LZiwZuQRhrmHYXLAZj6Q8gqS1Sfjbjr/h58Kf0aprtXS5REREdImSNSro9AJ251ZbuhSrxYjVirV0dGFfUS3GhntCIZOYvP30snQAwBifMSZv29ImBUxCom8ifsj7ATdH3oxYdaylSyIiIjrPlsIteGHPC6jvqMf0kOl4Jv6ZXu8UTH8uwDkAC4csxMIhC1HeXI5txduwtWgrfi78GT8X/gyFRIFxfuMwJWgKkvyTGNASERFZgWSNGv/acAIpWVpMi/a2dDlWiWGhFcsoqIFOLyApwjzTaNPL0+Fq54pBykFmad+SRCIRnoh7ArO+n4VXMl/BF9d8AYnY9IErERHR5WjoaMDLGS9jc8FmuNm54Y0Jb2Ba8DRLl2XTfJx8cEfUHbgj6g5UtlTil+JfsLVoK34p/gXbirdBLpYj0S8RU4OmYkLABLjIXSxdMhEREV1AiKcjgj0csCOrCoIgQCQSWbokq8Ow0IqlZp9Zr9DT5G3XtdfhZM1JTA2earMhWqhrKOZGzcXK4yvxbe63uCnyJkuXREREhNTSVDyf9jy0bVok+yfjn4n/hKe96f/W08V5OXrhtsG34bbBt6G6rRq/Fv+KLUVbkFqaipSSFEjFUozxGYMpQVMwKXASR3sSERH1M8kaNVamFSKnqhmRXs6WLsfqMCy0YqnZWvi6KhCmMv2UmIzyDAgQbHIK8tnuHXYvNuZvxDsH3sGUoCk82SciIotp0bXgP3v/g/U56+Ekc8ILY1/A9WHX82q4hXnae2K2ZjZma2ajtr0W24u3Y2vRVqSXpWPn6Z34d/q/EecT1xMcKhVKS5dMREQ04E3QqLAyrRApWVUMCy8DNzixUiW1rcivbkFSpMosbyLSy7vXK/S17bDQSe6EJSOXoK6jDu8fet/S5RAR0QC1t2IvZv0wC+tz1iPeJx7/d93/4YbwGxgU9jNKhRKzImfhoykfIeWWFLww9gUk+iUisyIT/0r/FyZ+PRF3/3w31p5ai+o2LqpORERkKWNCPWAnFSMlS2vpUqwSRxZaqZ05xhPQ8WZYr1AQBKSVpSHYJRi+Tr4mb7+/uTb0WqzNWou1WWsxK2IWNEqNpUsiIqIBor2rHW8feBtrTq6BvdQeT8c/jVs0t0As4vXc/s7VzhU3hN+AG8JvQFNnE1JKUrC1aCt2n96NjIoMvJTxEkZ4jcCUoCm4KvAqeDl6WbpkIiKiAUMhkyAh1ANpedVo7uiCkx3jr0vBM1ErlZqthVgEjAs3/RpGhY2FqGipQIJPgsnb7o/EIjGejn8agiDglcxXIAiCpUsiIqIB4Ij2CG7ecDPWnFyDWFUsvpn5DW4ddCuDQivkLHfGzLCZeGfSO0idk4rXk17HVUFX4Xj1cbya+SquWncV5m6ei1XHV6GsuczS5RIREQ0IyRoVdHoBabkc7X+pGK1aoS69AbvzqhET4AZXB5nJ208vM05BTvRNNHnb/VWURxRmRc7Cuux1+KnwJ0wPmW7pkoiIyEbp9Dp8ePhDfHrsU0hEEiwZuQTzoubZ7IZiA42jzBHTQ6Zjesh0tOpasev0Lmwt2oodpTtwWHsYb+x7A0M8hmBK8BRMCZqCAOcAS5dMRNSvaFu1OKI9Ao8uD0uXQlYuWaPGvzacQEq2FlOjvS1djlVhWGiFDpfWo6m9yyxTkAFjWCgRSTDae7RZ2u+vHh7+MLYUbsEb+97ABP8JcJA5WLokIiKyMVm1WXhm1zPIqsvCYOVgvDTuJUS4R1i6LDITB5kDpgZPxdTgqWjvasfust3YVrQNKSUpeHP/m3hz/5sYrByMKUHG4DDYNdjSJRMRWUSrrhW/FP+Cjfkbsad8DwyCAQqxArObZuOOqDvg7cighy5diKcjgjwcsCNLC0EQuBb0JWBYaIV2ZBuH0E6INP0UZJ1Bh72VezFMNQxOctPvstyfuSvcsXj4Yryc8TI+OfoJHh7xsKVLIiIiG9Fl6MLK4yvx/qH3IQgC7ou5D4uGLYJMbPoZAtQ/KaQKTA6cjMmBk9Gp78Se8j3YUrgFv5b8incOvoN3Dr6DCPcITAmagkGiQQhHuKVLJiIyqy5DFzLKM7AxfyN+Kf4FbV1tkIqkSPJPwiivUfj6xNdYfWI1vjj1Ba4NuRYLhyxEmFuYpcsmK5McqcKq9CLkVjUjgrsi9xrDQiu0M0cLZ4UUMf5uJm/7qPYoWnQtNr8L8sXcHHkz1mWvw8rjK3FD+A0IdAm0dElERGTlChoK8OyuZ3Gk+ghCXUPx8riXEe0ZbemyyILkEjmS/JOQ5J8EnV6HzIpMbC3ail+Kf8EHhz4AANzefjseHfko7CR2Fq6WiMh0BEHAqdpT2JC/AT8W/Nizc3yMKgYzQmdgWvA0uCvcAQCJ8kSU25dj+bHl+D7ve3yf9z2S/ZOxcMhCjPAaYclukBVJ1qixKr0I27OqGBZeAoaFVqahVYfDJfWYGuUNqcT0C6CnlaUBAMb4DMywUCqW4qm4p7Dw54V4fe/reG/ye5YuiYiIrJRBMODLU1/irf1voUPfgflR87F4+GIopApLl0b9iEwiw1i/sRjrNxbPJjyLfZX78OaeN/H5yc+RWZGJ18a/xqnqRGT1ypvLsalgEzbmbUReQx4AIMA5AA/EPIBrQ6+94CANkUjUc2HlsPYwVhxbgV+Lf0VKaQpiVbG4c8idmBAwgRuD0R9KCPWAXCpGSpYWi5I4MrW3GBZamV251TAIQFKkmdYrLE+Hs8wZQzyHmKV9azDKexSmB0/Hj4U/IrU0FUn+SZYuiYiIrExZcxme2/0cMisy4e/kjxfHvYiRXiMtXRb1c1KxFAk+Cfj3kH8jpTUFHx7+EHM2zsFjox7DrYNu5VpLRGRVmjqbsK1oGzbkb8C+in0QIMDNzg23aG7BzLCZGOY5rNevazGqGLw18S0UNBRg5fGV2JC3AQ9vfxihrqFYEL0AM0JnQCbh0h50Pnu5BGNCPZCWV43mji442TEG6w1+l6zMzhwtAGB8hOnXK2zoaMCx6mOYGDARUvHA/tVYMmoJUkpT8Frma0jwSYBcIrd0SUREZAUEQcC3ud/i9b2vo0XXgtmRs/HYqMe4aRZdEolIgntj7kWCbwKeTH0Sr2S+gt1lu/HvxH/Dw567gxJR/6Uz6LD79G5szN+IlJIUdOg7IBfLMSVoCmaEzsA4v3FXFOqFuIbgX4n/woOxD2LNyTX4Jusb/CPtH3jv0HuYFzUPsyJmDbi19+nPJWtU2JGtRVpuNXdF7iWO17UigiAgNVuLUE9HBChN/6Zjb8VeGATDgJ2CfDZvR28sGrYIxU3FWH1itaXLISIiK6Bt1WLxr4vxz7R/wlHmiI+u+gjPjXmOQSFdthhVDL6Z+Q2uC7sOqaWpmPXDLOw6vcvSZRERnUMQBBzRHsFLe17C5K8n46FfH8LPhT9jqOdQPD/meWy/ZTv+m/xfTAycaLLRf2oHNZaMXIItN23BoyMfhSAIeGPfG5i6birePvB2z1qIRIBx3UIASMnWWrgS6zGwh49ZmTxtM8oa2rEgMdgs7aeXpQPAgN3c5PfmRc3Dtznf4n9H/oeZoTPh5ehl6ZKIbJ7eoEdtZy10Bh13iSWr8mPBj3hxz4to7GzEzNCZeDL+SbjIXSxdFtkAJ7kTXhr3Esb5jcML6S/g/m334/bB3PyEiCyvpKkEG/M3YlP+JhQ1FgEAQl1DMS96Hq4JuQa+Tr5mr8FZ7ow7h9yJuYPnYkPeBqw8vhKfHP0Eq4+vxnXh12FB9AIEuQSZvQ7q30I8HRHk4YAdWVoIgsBlPXqBYaEVSc02Xh0xxxRkwLi5iZ+THwKcA8zSvrWRS+R4Iu4JPPjLg1i6fyleS3rN0iUR2RxBEJDfkI+M8gxkVmRib8VeNHY2QrRXBA97D6gd1PBy8ILaQQ1vR2+oHdQ9X/Ny8OKILbK4uvY6vJTxEn4u/BlKhRJvJb+FyUGTLV0W2aDpIdMRo4rBUzuf4uYnRGQxDR0N+LnwZ2zI24BD2kMAAA+FB+YOnouZYTMxWDnYIkGMXCLHrMhZuDHiRmwv3o7lx5ZjXfY6rM9ej6uCrsKdQ+4c0OvyE5AcqcKq9CLkVjVzV+ReYFhoRVJztJBJREgINf1aNSVNJShtLsVNkTcxZT/Lmd23NhdsxmzNbC5OT3SFBEFAaXMpMsszkVGRgczyTNS01wAAxCIxoj2i4SPxgaAQUNlaicqWSmTVZkEv6C/YnrPM2RgeOnqdFySe+Zq7nTtf18gsUkpS8Hza86hpr8HkwMl4LuE5ridHZuXr5Ivl05bjk6Of4MPDH+LWTbdiycgl3PyEiMyqU9+J1NJUbMjbgNTTqegydMFeao9rQ6/FjNAZSPBJ6Ddr3otFYkwOmoxJgZOwv3I/lh9bjq1FW7G1aCvivONw55A7keibyNfMAShZo8aq9CKkZGkZFvZC//gfTX+qXafHnvwajApSwtEMu/f0TEHmeoXn+fvovyO9LB2vZLyCtTPWQiKWWLokIqtS2VKJzIpM40d5JspaynqORbpHYnrIdMT7xGOk10g4y52Rm5uL8PDwnvvoDXrUtNegqrWqJ0A88/mZfw9WHURbV9sFn18mlp0TIp4dLp75mqeDJ6c9U681dTbhtczX8H3e93CWOePlcS9jRugMvvGgPiER/7b5yROpT3DzEyIyC4NgwMGqg9iYvxE/F/6Mps4miEVixHvHY2bYTEwKnARHmaOly7wokUiEUd6jMMp7FLLrsrHi2Ar8WPAjMisyoXHXYOGQhZgWPK3fhJxkfgmhHpBLxUjJrsI9SaGWLqff4/8MK7G/qA7tOgPGR5pnCnJ6Wbrxxd8n3iztW7MglyDMi5qHT499im+yv8GcQXMsXRJRv1bXXoe9FXuRWZGJjPIMFDYW9hwLdgnG7MjZiPOJw2jv0VAqlH/ankQs6Rk1OAQXnj4iCAKadE3nBIk9YWL31/Ib8nGg6sAFHy/C+dOezx6dyGnPdMae8j14bvdzqGipwFjfsXg+8Xl4O3JXPep7MaoYrJu5Di9nvIwN+Rsw64dZeHHcixjnN87SpRGRFctvyMfGvI3YXLAZp5tPAwA07hrMHDYT00OmQ+2gtnCFly7SPRKvjH8FDw1/CJ+d+Azrc9bjyZ1P4t2D7+KOqDtwY/iNPMcbAOzlEiSEemBPXg1aOrrMMgjLlvC7YyVSu3ftSYpQmbxtvUGPjIoMRHtEw9XO1eTt24JFwxZhQ/4GvHvwXUwLngZ3hbulSyLqN5o7m7G/cn/PtOKsuqyeYz6OPrg+7HrE+8RjtPdos4UqIpEILnIXuMhd/nD9rvaudmhbteeMSjw7XOzttOcLjU7ktGfb1qprxVsH3sKXp76EvdQe/xjzD9wUwaU7yLKc5E54efzLGOc3Di/ueRH3b7sfcwfPxSMjH+HmJ0TUazVtNfip8CdsyNuA4zXHARh3G144ZCFmhM5ApHukhSs0DV8nXzwR9wTuHXYvvsr6Cl+c/AKvZr6Kjw5/hNsG3YY5g+bwfZ6NS45UITVbi7S8GkyJ4gamf4RhoZXYka2Fp5McUT6m31nxeM1xNHU2IcEnweRt2woHmQMeG/kYntj5BN49+C7+MeYfli6JyGLautpwqOpQz7Ti4zXHe8I1pUKJ6cHTEecTh3jvePg7+/erMEUhVSDAJQABLhffyElv0KO2vfacALGqteqccPGQ9hDayns/7VntoIZDqwMC9AF8A2+FDlUdwjO7nkFxUzFGqEfgxXEvcjMw6leuCb0GsepYPLXzKaw5uaZn85Nw9/A/fzARDUhtXW3YXrwdG/M3Iq0sDXpBD0eZI24IvwEzQmdglNcom11+yU3hhvti7sP86Pn4Lvc7rDq+Ch8c/gArjq/AjeE3Yl70PPg5+Vm6TDKDZI0K/94IbM+qYlj4JxgWWoGqxnacqmjCDbG+EItN/6Y7rSwNAJDom2jytm3J9JDp+Dr7a6zLXoebIm9ClEeUpUsi6hM6vQ5Hq4/2jBw8rD0MnUEHAHCWOyM5IBlx3nGI845DmFtYvwoHL4dELIHKQQWVg6rX056rWqtQ0Vrxp9OeX8t6DaO8R2Gc3ziM8xuHIJegvugSXaZOfSfeP/Q+Vh5fCalIisdHPY65UXMhFoktXRrReXydfPHptE/xydFP8NHhjzBn0xw8NuoxzNHMsfrXZSIyDb1Bj72Ve7EhbwO2FW1Da1crJCIJxvqNxczQmZgQMAH2UntLl9ln7KX2uHXQrbg58mZsLdqK5ceW44tTX2Bt1lpMC56GO4fcCY1SY+kyyYRCPB0RqHTAjiwtBEHg38c/wLDQCuzMqQYAJEWafgoyYFyv0F5qjxhVjFnatxUikQhPxT2F2Rtn45WMV7B6+mpLl0RkFnqDHqdqT/WEgweqDvRsHmIvtUe8TzziveMR5xMHjbvGZq86/5HeTnvu0HcYg8SWCqScSsGpjlPYU74Hu07vAgAEOAdgrO9YjPMbh9Heo7leTj9ysuYknt71NHLrcxHtEY2Xx72MUDcuhk39m1QsxX0x9yHBJwFP7nwSL2e8jN2nd+Nfif/i5ifUa4IgoKq9CmGC9V8AJKPsumxszNuITQWbUNVaBQAY4jEEM8Jm4Orgqwf864NULMX0kOm4OvhqpJenY/mx5dhcsBmbCzZjrN9Y3Bl9J0Z7j+b/BxsgEomQrFFhdXoRcquauSvyH2BYaAV25hjXKxwXYfrNTVp0LTiiPYJEv0TIJNwJ9M9olBrMjpyNr7K+wsb8jRiMwZYuieiKCYKA3Prcng1J9lXsQ5OuCQAgF8sRq45FnHcc4n3iEe0ZzV2DL4GdxA4BzgEIcA6Ae7M7wsPD0aJrQUZ5Bnaf3o1dp3fhq6yv8FXWV5CJZRjpNbJn1GGoayhPSi2gsbMRX5d8jW/SvgEALI5djLuG3sXdEsmqxKpjz9v85KVxL2Gs31hLl0ZW4N2D7+Ljox/juvrr8FzCc1BIFZYuiS5DZUslfiz4ERvyNyC7LhsA4Ofkh0XDFmFG6AyEuIZYuML+RyQSIdE3EYm+iThefRzLjy3HtuJt2H16N4Z4DMGdQ+/EpIBJA/JCuS2ZqFFjdXoRUrK0DAv/AM98+zmDQcDOnGoM9nGB2tn0f6j3VuxFl9DFKciXYPHwxfip8Ce8uf9NvD3sbUuXQ3TJBEFASVNJz8jBzIpM1LbXAgAkIgmGeA7pCQdjVDF8k2BijjJHTAqchEmBkyAIAgoaC7CrdBd2l+3Gvop92FO+B2/sewM+jj4Y6zcW43zHId4nHk5yJ0uXbnMMggGFDYU4rD2MQ9pDOFx1GHkNeQCAcLdwvDzuZQz24EUhsk5nb37ywp4XcN+2+7j5Cf2prUVb8fHRj6EQK/BD3g/IqcvBWxPfgq+Tr6VLo15o0bXgl+JfsCFvAzLKMyBAgLPcGTdH3owZoTMwXD2cFyJ7KdozGv9N/i+KG4ux6vgqfJf7HZakLEGQSxDmR8/HdWHX8bXUSiWEekAuFSMluwr3JHHWyMUwLOznTpQ3oqalEzeN8jdL++ll6QCAMT5jzNK+LXK1c8VDwx/CC3tewAsnX8AHQR/A0970oz6JTKmipQIZ5RnGTUkqMlHRUgEAEEGEQcpBmBk6E3E+cRjpNRKOMkcLVztwiEQihLqGItQ1FPOi56Gtqw17K/b2jDpcl70O67LXQSqSIlYdi7F+YzHebzwi3SN5sn8ZWnQtOFp9FIeqDuGw9jCOaI+gsbOx57ivoy+mB09HoDgQixIXQS6RW7BaItO4JvQaxKhjuPkJ/anculw8s+sZKBVKvBL9Cg52HTSuf7lxDv4z4T+I94m3dIl0AV2GLuyv24+PUz/Gr8W/ol3fDqlYikmBkzAzdCbG+4/n37MrEOgSiOfGPIf7Y+/HFye/wFdZX+Hf6f/G+wffx9youZitmQ0Xuek3ISXzsZdLkBDqgT15NWjp6IKjHWOxC+F3pZ9L7Z6CPCHCTOsVlqdD7aDmMPRLdFPkTShsLMRnJz7DLRtvwZvJb2KYapilyyLqUdNWg70Ve3tGDxY3FfccC3UNxRzNHMT7xGOU1yi4KdwsVyidw15qjyT/JCT5JwEAihuLsbvMGBzurdiLfZX78PaBt6GyV2Gs31iM9RuLMT5j4GrnauHK+58zI2jPjBg8rD2MnPocGAQDAOOu1VEeUYhRxSBWHYsYVQzUDmoAQG5uLt9YkU3xc/LD8mnL8fHRj7Hs8DJufkLnaexsxCMpj0Cn1+GDyR/ArdkND4Y/iMHKwXh619NYtHURloxcgnlR8/g7048cqDyAp3Y+hbKWMgDACPUIXBt6LaYFT+O5gYl52nvi4REP466hd2F99nqsPrEabx94G58c/QQ3R96MuYPnwsuRu+tai+RIFVKztUjLq+GuyBfBsLCfS83Wwl4mwchgd5O3XdFSgYKGAtwQfgP/6F8isUiMv4/+O5Q6JT7K/wgLflqA5xKew40RN1q6NBqgGjsbsa9iX8+6g7n1uT3H/Jz88JeIv/TsWKxyMM/FBzK9QJdABLoE4tZBt6JD34EDlQew6/Qu7D69G9/lfofvcr+DWCTGMM9hPaMOB3sMHpC79bZ1teFY9TEc1h7uGTV4Zno9AKjt1ZgcOBkxqhjEqGIQ5RHFQJAGFKlYivtj7scYnzHnbH7y77H/hlKhtHR5ZEEGwYCndj6FosYiPBn3JEZ5j0JurvE8YlLgJHxx7Rd4ZPsjeGPfGzhefRzPJz7PDbksTG/Q45Ojn+CDwx9ALpZjTsAczBs9DwHOAZYuzeY5yhwxL3oebh10KzYXbMaKYyuw8vhKrDm5BjNCZ2Bh9EJuiGYFkjUq/HsjkJJVxbDwIhgW9mMtHV3YX1SHceGesJOafhFVTkG+csnqZCRqEvHo9kfxj7R/4HjNcTwx+gluFkNm16nvxL6Kffip8Cdkn8rGydqTPSOmVPYqzAidYQwHfeLg5+Rn4WrJFOwkdhjjOwZjfMfg8dGPo6y5zDjqsHQX9pTvwSHtIbx/6H2427kj0S8R4/zGIdE30SZDAEEQUNZS1jNi8JD2ELJrs9EldAEApCIpNEoNpodM7wkHfRx9eGGMCL9tfvJSxkvYmL8Rf/n+L9z8ZID74NAHSC1NxXVh1+G2QbeddzzUNRRfXPMFntn1DH4s/BF5DXl4a+JbDKYspKq1Ck/tfAqZFZmIcI/AG0lvwFBt4M+jj8kkMlwffj1mhs3EztKdWH5sec+F3OSAZNw15C7EqmMtXSZdRIinIwKVDkjJ0kIQBJ4jXgDDwn5sT34NdHoBSZFmmoLcHRYm+CaYpf2BIsojCl/N+AqP73gca7PWIrsuG0uTl3IdQzK5xs5G7CzdiV+Lf8Xust1o0bUAANzs3DA5cDLiveMR5xOHYJdg/sEbAHydfHFz5M24OfJm6PQ6HNIe6hl1uCl/Ezblb4IIIkR7RBs3SvEbh6GeQ61yB78OfQdO1pw0BoPd6w1q27Q9x5UKJcb7j++ZUhzlEQV7qb0FKybq35zkTnhl/CsY5zcOL+55kZufDGC/FP+CZUeWIcojCs8lPHfR8wcnuRPenPgmPjn6Cd47+B7mbJyD15NeZ8jcx3aW7sQzu55BXUcdbtHcgr+N+hsUUgVyq3P//MFkFmKRGBMCJmBCwAQcqjqE5ceWY3vJdqSUpGCEegTuHHInxvuPH5CzPvozkUiEZI0Kq9OLkKdtRriauyL/HsPCfiw12/hGaLwZ1is0CAbsKd+DwcrBNjnqpK+5K9zx0ZSP8Ob+N7H6xGrcsuEWLJ24FDGqGEuXRlauvLkc20u249eSX7G/Yj+6hC6IIMIw1TBMDJiIQEMgJg+bzBOQAU4mkWG092iM9h6NR0c+iqrWKuw+vRu7y3YjrSwNx44cw7Ijy+Aid8EY3zEY5zcOY33H9tsp6ZUtlb/tUKw9jJM1J6Ez6AAYT8oj3SMxKXCSMRxUxcLf2Z8BOdFluDb0WsSqY8/Z/OT1pNcR5hZm6dKoD+TX5+OZXc/A3c4dbyW/BYVU8Yf3F4vEWDRsEQYrB+OJnU/g/m33G9dwG3IXX4PNTKfX4e0Db2PViVVwljtjafJSTAmaYumy6Hdi1bF4Z9I7yK/Px4rjK7AxfyMW/7oY4W7hWBC9ANeEXGPpEuksZ8LClCwtw8ILYFjYj6XmVMPPzR5hKtPvTHqq9hTqOupwQ8QNJm97oJKKpXh89OOI8ojC82nPY+FPC/FM/DOYFTnL0qWRFREEAdl12fi1+FdsL9mOk7UnAQBysRyJfomYFDAJEwIm9Ixczc3NZVBI51E7qHFjxI24MeJGdBm6cKz6GHad3oVdp3fh58Kf8XPhzwCAQcpBGOtr3CglVh0Lmbjvl1DQGXTIqs3qGTF4WHsY5S3lPcdd5C5I8EnoGTU4xHMId+wmMqGezU+OfIyPjnyEWzYaRyvdormFAZANa+pswl+3/xXtXe14Z8o78HHy6fVjx/uPx1fXfoW/bv8r3j7wNk7UnMALY1/ga7OZlDSV4O87/o5jNccQo4rBa0mvcYmZfi7ULRQvjH0Bi2MXY83JNfgm+xs8u/tZvHvwXYxTjkNoRygcZY5wlDvCUepo/Px3H1xX2fzGhHpCLhVje1YV7h7PdSZ/75LDwhUrVuDOO+/Et99+ixtuuAFVVVWYN28e8vLyYGdnhw8++ABJScZdHC/3GAElta0oqG7BrXEBZjlROzMFOdE30eRtD3TXhl6LMLcwPLL9ETyf/jyO1xzHk3FP8gWfLkpn0OFA5QFsL9mO7cXbe3a0c7VzxXVh12FiwEQk+iZyMXG6LFKxFLHqWMSqY7F4+GLUtNUgrSzNOOrwdBo+PfYpPj32KRxljkjwScA4v3EY5zcO3o7eZqmnuq26JxQ8XHUYx2uOo0Pf0XM83C0csyJmGdcaVMcg2CWYgTiRmUnFUtwfez/G+Bo3P3kp4yXsPr0b/xr7L85AsUEGwYCndz6NwsZC/H303xHnE3fJbQS6BOLzaz7HP9L+gZ8Lf0Z+fT7emvgWgl2DTV/wAPZTwU94Pv15tOpacffQu/FA7AMWubBHl8fL0QuPjXoM9wy7B19nfY01J9Zgfel6oPTPHysTy3qCQweZA5xkTnCQOcBR6ggnuRMcpA4XDBkv9KGQKHjx5wLs5RLEhyiRkV+Llo4uONpxLN3ZLum7UVhYiI8//hgJCb+tcffkk08iISEBP/30E/bu3Ysbb7wRBQUFkMlkl32MgNQc801BBoxhoZ3EDsPVw83S/kA3SDkIX137Ff6W+jd8k/0Nsuuy8Wbym/12yh/1vRZdC3af3o3tJduRWpqKxs5GAMYRHnMHz8WkwEkYrh4OqZh/tMi0POw9MDNsJmaGzYRBMOBEzYmetQ63l2zHL8W/AADCXMOM05X9xmKk18jLuuDRZehCTl1OTzh4qOoQSpt/O0N2kjlhhHoEYtWxiFHFYKhqKFzkLibrKxFdmlh1LL6Z+Q1eyngJm/I3cfMTG7Xs8DKklKbg2tBrMXfw3Mtux0HmgP8k/QfRHtF468BbuHXTrXh1/KuYEDDBhNUOTG1dbXgt8zWsz1kPD4UHliYv5SAPK+Yid8HdQ+/GvKh5SD2WCg8fD7ToWtCsa0arrhUtupY//GjtakVxWzFau1rR1tV2yc8vFomNIxjPGsl4TgApczzn83M+LvA4W7qIm6xRY2dONdLyargr8u/0+l2owWDA3XffjXfffRePPfZYz9e//vpr5OYaF1QdPXo0fH19sWPHDlx11VWXfYyM6xWKRcDYMNNvktHW1YYDVQcw2ns0F7E2IzeFGz666iO8c+AdrDi+ArdsvAVLk5dyV6wBTNuqRUppCrYXb8ee8j09a7BFeURhYsBETAyYiEj3SF75oz4jFokxxHMIhngOwX0x96GhowHp5enYVboLu8t2Y9WJVVh1YhXspfaI847r2SjlYjsu1rfX40j1kZ4pxUerj55zUhvsEozrwq7rCQfDXMOscsMVIlvmLHfGq+NfxTi/cXhpz0s9m588OvJRzpKwAduLt+ODwx9gsHIw/jnmn1d8ziESibBwyEIMUg7C46mPY/Gvi/FAzAO4N+ZemwoU+lJ2XTYe3/E48hvykeibiJfGvcSNE22EXCJHsGMwwtXhl91Gl6ELrV2t54SMZ4eOF/u8pasFLZ3GfytaK4whpK4VAoRLruFCoxqTXJMQHn75/bKUiRoVXtgIpGRVMSz8nV6HhUuXLsXYsWMxcuTInq/V1NRAp9PB2/u3qUrBwcEoLi6+7GMEdOkNSMutQWyAG1wdTD/S8kDlAegMOl6d6gNSsRRLRi3BYI/B+Mfuf2DhzwvxVNxTmK2ZbenSqA8IgoCChgL8WvIrthdvx5HqIwAAqUiK0d6jMTHQGBCaa7on0aVytXPF1cFX4+rgq3vWz9x5eqdxs5TTu7GjdAcAIMglCOP8xiHeOx4nK05iZcVKHNYeRmFjYU9b9lJ7DPEcgliVMRgcphoGd4W7hXpGRJdqRugMxKp+2/xkb8VevJb0Gjc/sWIFDQV4etfTcLNzw5sT3zTprvFjfMdg7Yy1eGT7I/jg8Ac4UXMCL49/Gc5ybhrQW4Ig4Jvsb/D63tehN+jx6MhHsSB6AUNXOodULIWL3MUkMzEMggHtXe0XDBnPhIktXS1o7mxGa9eFR0DWttciqy4L+bX5uCP+Dqv7fQ3xdESg0gEpWVoIgsBBG2fpVVh47NgxrF+/Hqmpqeau5zxLly7F0qVLe243Njb2jEi0RdXV1ThW0Yqmji5Ee0jM0tfNBZsBAH5dfn32vayuru6T5+lrve1XBCLw8tCX8drJ1/DCnhewp2AP7gm9p1+uOTLQf1ZXSi/okd2UjYyaDGTWZqK83bhRg73EHuM8xyFOGYcR7iPgKDUuAt5c3oxcXP7/Q1v8edlinwDr7JcEEiTbJyM5PBmtwa040nAEB+sO4kDdAXx+8nN8fvLznvt62XkhSZUEjbMGg5wHIcgxCBJR96jBDqCmtAY1qLFQTy6NNf6seoP9sh79qU/PRjyLdYp1+Lrka8zeMBsLghfgau+rL+sNVX/qlylZQ79au1rx9yP/z959x1VV/nEA/1y2IkMQGYIgw71w7z0wQXNbjtKcaZkr/ZUNG5YN07JyVGqZW0vBkXsjbnEiQ8QBslQUQbjw/P54GFpagOC55/B5+zovuPdc4Pv1Offec7/nGW/jQeYDfFDrA6TFpf3r+UdRc/qw6of4MfJH7L2+F33/6IvpNabDreyTe6MrwVDbKlWfiu8jvkdwUjAqmlfEpGqTUM2iGqIiowr084aa17PSYl6GnJN5zj875MxVa5Kz/ftC6fgt+jdsuLEBG09sRB3bOiUdZrHzdTLDxgt3sPv4BbiXf3zkpSG3V0krULHwwIEDiI6Oho+PDwAgLi4Oo0aNwsyZM2FiYoK4uLi8XoLR0dGoXLky7O3ti7Tv7yZNmoRJkybl3XZ1dVVl99bCOByVBQB4sVk1eLsXfy+Mixcuwt7CHh3rdnyulXOttltB8/KGNxpXa4y397+NHbE7cCv7Fr5p9w0qlq1YwhEWXmlvq8JK16cj+GYw9lzbg33X9yE5PRmAXJF2QLUB6ODWAY2dGsPUuGSKw1psLy3mBKg/r7qoi8EYDCEEou5G4VjcMejv6uFX109zQ6TU3lZPw7zUw5ByetfnXXSP747pB6ZjUdQiXM64XOTFTwwpr+JkyHlli2xM3DMRN9JuYEqjKehdq3eBfq6oOX1X9TusuLQCXx77EtPPTsenrT5FJ3fDmWrK0NrqTMIZvL3vbdxMvYnO7p3xYYsPi9RrzNDyKi5azEtrOQ2tMBQbbmzAyYcn0cu7l9LhFFpPvRU2XjiOK+ll0NH7n6sia629CqpAfUTHjh2L2NhYREdHIzo6Gs2aNcOiRYswduxY9OvXDwsWLAAAHDt2DDdu3EDbtnJS26LuK+32hyfC2sIE9Vxtiv13JzxIQPjtcDR3ac4utgqwtbDFj51+xPDawxGaEIoBQQNwKv6U0mFREdxOv40/I/7EhN0T0GZ1G7y55038EfEH7CzsMKruKKzqvgo7++7EjGYz0KJSixIrFBIpQafTwcvWCwOrD0Qz+2aaKxQS0T/lLn7S3bM79l7fiz6b+uDwjcNKh0UFsDh0MXZf241uVbphaM2hJf73dDodBtUYhMVdFsPCxAIT907EvJPzkJWdVeJ/W02yRTZ+PvszXtn6CpLSk/B+8/fxdduvudAXqY6XrRe8LL2w4+qOIi3AorTmnhVgZmKEvWEJSodiUJ55mc3Zs2djyJAh8PHxgZmZGZYvX563onFR95VmKelZCL1+B11rOcHEuPjH+x+JPQJAzitCyjA2MsbEhhPz5jEcvm04pjeZjv7V+rOAa+CupVyT8w9e24NT8aeQLbJhpDNCg4oN5AIllds/deEHIiIitXt08ZNPjnyC0TtHY0jNIXirwVtc/MRA7b++H9+f/h7VylfDzBYzn+u5ZmOnxljtvxoT90zET2d/wsWki5jdZjZszIu/Q4TaJKYl4t2D7+LwzcPwsvHCl22/hE95H6XDIiqythXb4pcrv2Dvtb3oVqWb0uEUShkzYzStYoeQqGSkPtTD0vyZy2SaUKT/hb179+Z97+joiO3btz/xcUXdV5qdvJmKbAG0qepQIr8/+GYwAKCZc7MS+f1UcH4efvC08cSE3RPwScgnOJ90Hu82e5crVBuQbJGNC0kXsDtGFggj7si5fcqYlMlbvbiNaxsu2kBERKVK7uIn0w9Mx28XfsPR2KNc/MQAXU25iun7p8Pa3Bpz288t1gVNCsrJ0glLuy3Fp0c+xR8Rf2Bg0EDMbT8X1eyqPfdYDMXhm4fxzoF3kJSehD4+fTCtyTRF2oaoOLWu0BrLopchMDJQdcVCAGhXrSIOhCciODIJnbgqMoBi6FlIxevE9QcAgNY+xT+kSwiB4NhgeNt6G+Q8eaVR1fJVscp/Fabtn4Y/Iv5AxJ0IzGk3h6vjKigzKxNH445iz7U92BOzB/Fp8QAAOws79PbpjfZu7dHMuRksTP5jpl8iIiINc7VyxVK/pVgUuggLQxdiQNAATG00lSMlDERqZiom7J6AVH0qfuz0I1ytXBWLxdzYHDNbzETtCrXx2dHPMGTrEMxsMVOVBYVnkZmdie9PfY9fzv2CsqZl8UWbL0rd/wFpl62ZLVq4tMDhm4eRmJaouilq2lVzwMdBwN7L8SwW5mCx0IAIIXD8Rio8HSzhWr5ssf/+8DvhSExL5JuSgbExt8H3Hb/H/NPz8dPZnzAgaADmtJuDho4NlQ6t1EjJSMHB6wex+9puHLxxEKmZqQAAD2sPDPMchvaV26NuhbowNjJWOFIiIiLDYWJkgtfrv47mLs0xff90fBLyCQ7ePIiZLYq2+AkVDyEEZhycgci7kZjYcCJauLRQOiTodDr0r9YfVctXxaS9k/D2/rdxPvE83mr4FkyMtP+R9Mb9G5i2fxrOJJxBbfva+KLtF5y6hjQnwCsAB24cwNYrWzGk5hClwykUzwqWcLMrgz2XEiCE4EUvFHCBE3o+IuLvIyFVjzY+JTsEubkz5ys0NMZGxpjQYAK+bvs10vRpGPHXCKy8tBJCCKVD06y41DisuLgCI7ePRNtVbTHtwDT8Ff0XvGy9MKHBBGx8cSMCewViUqNJ8K3oy0IhERHRU/hW9MW6HuvwQpUXsPcaFz9R2s/nfsbOmJ3o6tEVw2oNUzqcx9SvWB+r/VejvkN9LLuwDGN2jMHt9NtKh1WidlzdgX6B/XAm4QxerfUqfu32KwuFpEnt3drD0tQSgZGBSodSaDqdDu2qVsSNO2mITLivdDgGQfuXcVRkf3giAKBN1ZLpshscGwxTI1P2WDNgXTy6oIpNFUzYMwGzQmbhQtIFzGg2g/MYFgMhBMKSw+QCJTF7cDH5IgDAzMgMzV2ao0PlDmjn1k51XeaJiIgMgZWZFWa3mY1WlVrh05BPufiJQg5cP4BvT34Ln/I++KjFRwbZO8ahrAN+6foLZh+bjdVhqzEgaADmtp+LmvY1lQ6tWKXr0/HV8a+wOmw1ypuXxw8df0Br19ZKh0VUYixMLNDFvQv+iPgDkXciVTePbbtqDvjtyFXsDUuAd0UrpcNRHHsWGpCk+w9R1tQIzTzti/13P8x6iBNxJ+Bb0RdlTYt/iDMVH5/yPljZfSVaVWqFPyP+xCtbX0FcapzSYanWtXvXMOfEHIw5MQZ9A/vih9M/4Mb9GwjwDMCcdnNwYOAB/NDpB/St2peFQiIiomcU4BWAtQFrUc+hHn678Bte3vwyIu9EKh1WqRCTEoNpB6ahnFk5zGs3z6DP+U2NTTGj2Qx81OIjJKUlYejWodgUuUnpsIpN1J0ovLzlZawOW42mTk2xrsc6FgqpVAjwCgAAVfYubO5lDzNjI+wNS1A6FIPAnoUG5G2/6gioYoyyZsXfLKfjTyM9Kx3NXTgEWQ1szG0wv8N8fH/6eyw+uxgDggbgq7ZfobFTY6VDU4Ws7CwcvHEQq8JW4dCNQxAQcDB3wOAag9HerT18HX1hamSqdJhERESa5GblhqV+S7EwdCEWhS7KW/ykgXEDpUPTrAeZDzBhzwTcz7iPHzr9ADdrdQxz7eXTCz7lffDWnrfw7sF3cT7xPKY0nqLa8zQhBP6M+BOfHf0MD7Me4g3fN/Ba7dc4nQ2VGg0dG8LZ0hmbr2zGmw3ehJFOPf3TypqZoKmnHUKikpH6UA9L89JdLlNPy5USpsYlM1Qgb75CFgtVw9jIGG82eBPftPsG6fp0jNo+Cr9f/J3zGP6L5PRk/Hz2Z3T/ozvG7x6P4JvB6Fi5IxZ3WYyFDRdiWpNpaOLcRLUnoERERGphYmSCcfXHYUnXJbC3sMcnIZ9g9qXZuJdxT+nQNEcIgfcOvYeIOxF4s8GbaFWpldIhFUrtCrWx2n81Gjk2wopLKzDirxFITEtUOqxCu59xH9MOTMP7h9+HjbkNlnRdglF1R7FQSKWKkc4I3T27Iy41DsfjjisdTqG1q1YRGVnZCI5MUjoUxbFYWEocvnkYtua2qGFXQ+lQqJA6uXfCiu4rUMmqEj4/+jlmHJqBdH260mEZDCEEziScwf8O/A+d1nbC3JNzka5Px+i6o7GtzzZ80/4bNHNuZpBz9hAREWldA8cGWNtjLbp5dENIcghe3vwyou5EKR2Wpvxy7hdsv7odnd0747XarykdTpHYl7HHoi6LMLjGYJyMP4kBgQNwJuGM0mEV2PnE8+gf1B9br2xFB7cOWBewDg0c2ZOWSqcATzkUWY1TC7SrJheb3Xs5XuFIlMdiYSlwO/02LiVfQlPnpqrqBkz5vGy9sKL7CrRxbYNNkZvwyrZXEHs/VumwFPUg8wHWX16PAUEDMHjLYARFBaGuQ1182fZL7Oi7A+N9x8PJ0knpMImIiEo9azNrzG4zG8OrDMe1e9fw0uaXsCtml9JhacKhG4cw7+Q8eNt645OWn6j64qipkSmmNZmGz1p/hpSMFAzbNgzrLq9TOqx/lS2ysez8MgzeOhhxqXF4p+k7mNt+LmzMbZQOjUgxnraeqGlfEzuu7kCaPk3pcArFs4Il3OzKYG9YQqkf0cfKUSkQEhsCAYEWLi2UDoWegbWZNb7r8B1G1x2NC0kXMCBoAI7FHVM6rOcu+m40Zh+djU5rO+HD4A9xNeUqBlQbgA09NmCp31L4efjB1JjDjImIiAyJTqdDgEsAFnVeBHNjc7y15y3MPzUf2SJb6dBU69q9a3h7/9soZ1oOc9vPNegFTQrD39Mfv73wGyqWrYiZwTMxM3gmMrIylA7rH5LTkzF+13h8dfwruJZzxYruK/BS9ZdUXbAlKi49vHrggf4B9sTsUTqUQtHpdGhXtSKu305DZEKq0uEoisXCUuDwzcMAgObOnK9Q7Yx0RhjvOx5z289FRnYGRm4fieUXlmv+qoc+W49dV3dh5PaRCPgzAMsvLoejpSPebfoudvXbhRnNZsCnvI/SYRIREdF/aOLcBKv9V6OGXQ0sDF2IN3a/gZSMFKXDUp0HmQ/w1p63cC/jHj5v8zncrd2VDqlYVberjlXdV6GZczOsu7wOw/4ahlupt5QOK8+xuGPot6kfDtw4gB5ePbDafzWq21VXOiwig+Hn4QdjnTECo9S3KnLeUOSw0j0UmcVCjRNCIDg2GB7WHnAu56x0OFRMOlbuiBUvrICblRtmH5uNdw6+o7ou3gWRmJaIhWcWwm+9H97a+xaOxx2Hn4cflnRdgg09NmBg9YEoZ1ZO6TCJiIioEJzLOePXbr+ih1cP7L++Hy9vfhmRdyKVDks1hBD44PAHuHz7Msb7jkcb1zZKh1QibC1ssaDTAgyvPRyhCaEYEDQAJ2+dVDQmfbYe35/+Hq/99RruZd7DrFaz8GmrTzXTq5OouNiXsUfLSi0RfDNYdQsWNfeyh5mxEfZdTlA6FEWxWKhx0SnRiEuN4yrIGuRp64kV3VegnWs7BEUF4ZWtr+Dm/ZtKh/XMhBA4cesEpu6bis5rO2P+6fkQEBhXfxy2992OL9t+iUZOjTjEg4iISMUsTCzwSctPML3JdFy/dx0vb34ZO6/uVDosVVh2fhm2RW9Dx8odMaLOCKXDKVHGRsaY2HAivmz7JR7oH+C1v17DyksrFRlVE5cah9f+eg0LzixAdbvqWOO/BgFeAc89DiK1CPAMQJbIwtYrW5UOpVDKmpmgqacdQqKSkZZZeqfKYLFQ4zgEWduszKwwr8M8vF7vdVxMvogBQQMQEhuidFhFkpqZitWXVqP3pt54ddur2Ba9DQ2dGuKbdt/grz5/YUy9MXAo66B0mERERFRMdDodBtUYhMVdFsPCxAIT907Etye/RVZ2ltKhGazgm8H45uQ38LTxxKetPi01ixf6efjh9xd+h3M5Z8wKmYUZh2YgXZ/+3P7+npg96BvYFyfjT2JwjcFY/sJyeNh4PLe/T6RG7dzaoZxpOQRGqm8octuqDsjIysbpmw+UDkUxpePdpRQ7cvMITHQmaOzUWOlQqIQY6Ywwtv5YfNv+W2RmZ2LUjlFYdn6ZauYxjLgdgU+PfIqOazvik5BPcCv1FgbXGIyNL27ET11+Qif3TjAxMlE6TCIiIiohjZ0aY7X/atSyr4XFZxdj/O7xuPvwrtJhGZzr965j6v6pKGtSFvPaz4OlqaXSIT1XPuV9sLL7SrSu1BqbIjfhlW2vIPZ+bIn+zYysDHx+9HO8uedNAMC37b/FtCbTYGZsVqJ/l0gLLEws0Nm9My4mX0TE7QilwymU9tUrokkVO5gal97RbCwWalhmdiaOxh1FXYe6nNetFGhfuT1WdF8Bd2t3fHX8K0w/MN1g5zHMzM7EtuhtGLZtGHpt6oVVYavgWs4VHzT/ADv77cS0JtPgaeOpdJhERET0nDhZOmFZt2Xo6dUTB28cxEubX0L47XClwzIYafo0TNw7EXcf3sXnrT8vtb3abMxtML/jfIyuOxoXki5gQNAAHI09WiJ/K/puNAZvGYzfL/6OBhUbYF3AOrSv3L5E/haRVuUO1VfbQideDuWwZnRzNHItXRdlHsVioYaFJoTigf4Bmrk0UzoUek48bTyx4oUVaO/WHluubMHQrUNx/d51pcPKE5cah+9Pf48u67pg6r6pOJNwBv6e/vit229YG7AWfav25QTRREREpZS5sTk+bvkx3mn6DmLvx2LQlkHYHr1d6bAUJ4TAh4c/xKXkS3i9/uto69ZW6ZAUZaQzwnjf8Zjbfi4ysjNKZFRNYGQg+gf1x6XkSxhbbyx+7voznCydiu33E5UWDR0bwtnSGZujNiNblN75/9SIxUINC74ZDABo4dJC4UjoeSpnVg5z28/FuPrjEJYchoGbB+YdC0oQQiAkNgQT90yE33o/LDizAGZGZpjQYAJ29N2Bz1p/hvoV63PBEiIiIoJOp8NL1V/CT11/QhmTMpi8bzLmnphbqucx/O3Cb9hyZQvaubXD6LqjlQ7HYHSs3BEruq+Am5Ubvjr+FaYdmPbMo2oeZD7AuwffxTsH34GVqRV+7vozXq//OqfEISoiI50R/D39cevBLRyLO6Z0OFQILBZqWHBsMKxMrVDLvpbSodBzZqQzwph6Y/Bdh++QlZ2FMTvHYOm5pc91HsOUjBT8fvF39NzYEyO2j8DOmJ1o5tIM33X4Dlt6b8GIOiNgX8b+ucVDRERE6tHQsSFW+69GnQp18PO5nzFu17hSOY9hSGwI5pyYAw9rD3zW6rNSs6BJQXnaeGJl95Xo4NYBW69sxeAtg3Ht3rUi/a5LyZcwIGgANkVuQhvXNljXYx3nfScqBv5e/gCgyoVOSjO+22jU3Yd3cS7xHJo4N+GVsFKsrVtbrOy+Eh7WHvj6xNeYtn8aHmSW7IpOYclhmBk8E53WdsLnRz9HcnoyXq31Kjb32owFnRagnVs7GBsZl2gMREREpH5Olk5Y4rcEvbx74dDNQxgYNBCXb19WOqzn5ub9m5i6byosTCwwr8M8zkH+FOXMyuGb9t9gfP3xCL8djoFBA3H4xuEC/7wQAisursDLm1/G9fvXMbXRVMzvMB/lLcqXYNREpYenjSdq2dfCjqs7DHZOffonFgs16ljcMWSLbA5BJnjYeGBF9xXoWLkjtkZvxZCtQ4p8xfVpMrIyEBQVhCFbhqBvYF+su7wOXjZe+Ljlx9jZdycmN5qMytaVi/VvEhERkfaZG5tjZouZmNF0BuJS4zB4y2Bsi96mdFglLl2fjrf2vIXbD29jVqtZXPjtPxjpjDC63mjM7zgfQgiM2TkGP5396T9H1dx9eBcT9kzAZ0c/g5OlE5Z3W46htYZyehyiYhbgFYAH+gfYHbNb6VCogFgs1KjcOeqaOzdXOBIyBJamlpjTbg7e8H0j/4rrzYJfcX2am/dvYt7Jeei8rjP+d+B/uJh8ET29emJl95VY6b8SL3q/CAsTi2LIgIiIiEornU6HAdUH4OeuP6OsSVlM3TcVc07M0ew8hkIIzAyeiYvJFzGm3hh0qNxB6ZBUo41rG6zyXwUvWy/MOzkPk/dNRmpm6hMfe/LWSfQN7Is91/bghSovYI3/GtSqwOmbiEqCn4cfTHQmqlsVuTRjsVCjDt88jErlKsHN2k3pUMhAGOmMMKruqLwrrmN3jsUv534p9DyG2SIbh24cwhu73kC3Dd3w09mfYGlqiSmNpmBn3534pNUnqF2hdgllQURERKVVA8cGWO2/GnUd6mLJuSUYu3OsJucxXHFpBYKigtDWtS3G1hurdDiqU9m6Mn5/4Xd0ce+CHVd3YNDmQbiacjVvf1Z2FhaeWYhhfw3D3Yd38VGLj/B56885zJuoBNmXsUfLSi0RfDMYiWmJSodDBcBioQZdS7mG6/evcwgyPVEb1zZY6b8SVayr4JsT32Dq/qkFmsfw7sO7WHZ+Gfz/8MeYnWOw7/o+tKnUBj92+hFBvYLwSq1XYGthW/IJEBERUanlaOmIJV2XoI9PHwTHBmNA0ACEJYcpHVaxORZ3DF8e+1IuaNKaC5oUVVnTsviq7VeY1HASrqRcwUtBL2HftX1IfpiMUTtGYf7p+fC29caq7qvQy6cXhx0TPQf+Xv7IFtnYErVF6VCoALjyhQYFx+YMQXbhEGR6Mndrd/ze/Xe8d+g9/BX9F6LuRmFeu3lP7Il6PvE8VoWtwtYrW/Ew6yHsLOwwos4I9K3aF5XKVVIgeiIiIirNzIzN8GGLD1GrQi3MCpmFwVsG46OWH6FblW5Kh/ZMYu/HYsq+KTA3Nsfc9nNhZWaldEiqptPpMKz2MFS3q46p+6di/O7xsDS2RGpWKgZUG4ApjaZwuhyi56idazuUMy2HoKggDK01VOlw6D+wWKhBwTeDYaQzQhOnJkqHQgbM0tQSX7f9Gj+f+xnfnvwWAzcPxBdtvoAjHJGuT8e26G1YfWk1ziWdAwDUd6iPAdUHoIt7F5gZmykcPREREZV2/ar2g4+tDybunYi397+NC0kXMKHBBJgYqe8jTro+HW/tfQvJ6cn4pt038LL1UjokzWju0hyr/Vdj4p6JiLkbg2/afYNO7p2UDouo1LEwsUAXjy7YEL4BEbcj4F3eW+mQ6F+o752U/pU+W4+QuBDUtq8NG3MbpcMhA6fT6TCizghUt6uOt/e/jbE7x6KFfQucO34Odx/eRRmTMujj0wcDqw9EdbvqSodLRERE9Jj6Fetjtf9qTNo7CUvPL8Wl5Ev4ss2XqpoaRQiBj498jAtJFzCyzkgWskpApXKVsMp/FS5evoha7lzEhEgp/p7+2BC+AYFRgZjYcKLS4dC/4CQYGnM+6TzuZdxDM5dmSodCKtKqUius6i5XjjuUdAjlzctjepPp2NlvJz5s8SELhURERGSwKpatiF+6/oJ+VfvhSOwRDNw8EJeSLykdVoGtCluFTZGb0LpSa4yrP07pcDTLSGcEc2NzpcMgKtUaOjaEi6ULgqKCNLuivVawWKgxwTdz5it05nyFVDiVrStjRfcV+KreV9j04iYMqjEI1mbWSodFRERE9J/MjM3wfvP38UHzDxD/IB5DtgzB5qjNSof1n47HHccXR79AZavK+LzN5zA2MlY6JCKiEmOkM0J3z+6IfxCPY7eOKR0O/QsWCzUm+GYwypqURT2HekqHQipUxqQMvMp5cUU4IiIiUqW+Vftiid8SWJtZY/qB6fjy2JfQZ+uVDuuJ4lLjMHnfZJgam2Ju+7m8SEtEpUKAVwAAIDAyUOFI6N+wWKghqZmpCE0IRWOnxjA1NlU6HCIiIiKi566eQz2sDlgN34q++PXCrxizYwyS05OVDusxD7MeYtLeSUhOT8YnLT+BT3kfpUMiInouqthUQW372th5dSfS9GlKh0NPwWKhhhyLOwa90KO5C4cgExEREVHpVaFMBfzc5WcMqDYAIXEhGBg0EBeSLigdFgC5oMmnRz7F2cSzGFFnBLp4dFE6JCKi58rfyx8P9A+wO2a30qHQU7BYqCF58xWyWEhEREREpZypsSlmNJuBmS1mIjEtEUO3DjWIYW9rwtbgj4g/0NKlJcbXH690OEREz123Kt1gojNBYJTyr8n0ZCwWasjhm4fhWNYRVayrKB0KEREREZFB6O3TG8v8lsHG3AbvHHwHs4/ORmZ2piKxnLx1Ep8f/Ryu5Vwxu81sLmhCRKWSnYUdWlVqheCbwUh4kKB0OPQELBZqRFxqHKJTotHcpTkXpyAiIiIiekQdhzpY7b8aDSo2wPKLyzF6x2gkpSU91xhupd7CpL2TYGpsinkd5sHG3Oa5/n0iIkPi7+WPbJGNLVe2KB0KPQGLhRqROwS5hUsLhSMhIiIiIjI8FcpUwE9dfsJL1V/CsbhjGLh5IM4nnX8ufzsjKwOT9k1CUnoSPmr5EaqWr/pc/i4RkaFq59YOVqZWCIoKUjoUegIWCzXi8M3DAICmzk0VjoSIiIiIyDCZGpvinabv4OOWHyM5LRlDtwzFpshNJf53Pzv6GUITQjGs9jD4efiV+N8jIjJ05sbm6OLRBZeSLyH8drjS4dDfsFioAdkiGyGxIahhVwN2FnZKh0NEREREZNBe9H4Ry7otQ3mL8nj34Lv4LOSzEpvHcO3ltVh3eR2aOzfHBN8JJfI3iIjUyN/THwC40IkBYrFQAy4lX8Lth7e5CjIRERERUQHVrlAbq/1Xo6FjQ6y4tAIjt48s9nkMT8efxqyQWahUrhK+aPMFFzQhInpEA8cGcLF0weaozcjKzlI6HHoEi4UakDsEmcVCIiIiIqKCsy9jj8VdFmNQjUE4cesEBgQNwLnEc8XyuxMeJGDS3kkw0ZlgXvt5sLWwLZbfS0SkFUY6I/h7+SP+QTyOxh1VOhx6BIuFGnDk5hFYGFvAt6Kv0qEQEREREamKqZEppjeZjk9bfYrb6bfxytZX8GfEn8/0OzOzMjFp7yQkpCVgZouZqGZXrXiCJSLSmADPAADgQicGhsVClUvTp+Fk/Ek0dGwIc2NzpcMhIiIiIlKlHl498OsLv8KujB3eO/QePj3yaZHnMfz86Oc4nXAar9R8BS94vlDMkRIRaYeHjQfqVKiDHVd34EHmA6XDoRwsFqrciVsnkJmdySHIRERERETPqJZ9Laz2X43GTo2xKmwVRvw1AolpiYX6Hesvr8eay2vQ1Kkp3mr4VskESkSkIf6e/kjTp2H3td1Kh0I5WCxUueCbwQA4XyERERERUXGws7DDos6LMKTmEJyMP4kBQQMQmhBaoJ8NTQjFpyGfwsXSBV+2/RImRiYlHC0Rkfp1q9INJjoTBEVyKLKhYLFQ5YJjg1GhTAX42PooHQoRERERkSaYGJng7cZvY1arWbj78C5e3fYq/gj/419/JjEtERP3ToSRzghz289FeYvyzylaIiJ1K29RHq0qtUJwbDASHiQoHQ6BxUJVS3iQgPDb4Wjm3Aw6nU7pcIiIiIiINCXAKwC/dvsVDmUc8P7h9/HJkU+QmfXPeQwzszIxee9kxD+IxwfNP0AN+xoKREtEpF4BXgHIFtnYcmWL0qEQWCxUtSOxRwAALVxaKBwJEREREZE21bSviVX+q9DUqSlWh63Ga9tf+8c8hl8c+wIn409icI3BCPAKUChSIiL1auvWFlamVgiMDFQ6FAKLhaqWO19hM+dmCkdCRERERKRd5S3KY0HnBRhacyhOxZ/CgMABOJNwBgCw69YurApbhcZOjTGp0SSFIyUiUidzY3N08eiCsNthuHz7stLhlHosFqqUEALBscHwtvWGQ1kHpcMhIiIiItI0EyMTTG08FZ+3/hwpGSl4ddurmHdyHhZGLoSTpRO+avsVTI1MlQ6TiEi1cntmc6ET5bFYqFLhd8KRmJbIIchERERERM9Rd8/u+O2F3+BY1hE/nf0JADC3/VzYWdgpHBkRkbr5VvRFpXKVsDlqM7Kys5QOp1RjsVClcocgN3dprnAkRERERESlS3W76ljVfRX6Vu2LKdWmoJZ9LaVDIiJSPSOdEbp7dkd8WjyOxh1VOpxSjcVClQq+GQxTI1M0dGyodChERERERKWOrYUtPmj+AZrYN1E6FCIizQjwzBmKHMWhyEpisVCFHmY9xIlbJ9CgYgOUMSmjdDhERERERERERM/Mw8YDdSvUxY6rO/Ag84HS4ZRaLBaq0On400jPSkczF66CTERERERERETa4e/ljzR9GnbF7FI6lFKLxUIVOnzzMADOV0hERERERERE2uLn4QcTnQmHIiuIxUIVCr4ZDFtzW9Swq6F0KERERERERERExaa8RXm0cm2FI7FHkPAgQelwSiUWC1UmOT0Zl5IvoZlzMxjp2HxEREREREREpC0BngHIFtnYcmWL0qGUSqw2qUxIbAgEBIcgExEREREREZEmtXVrCyszKwRGBiodSqnEYqHKBN8MBgA0d2axkIiIiIiIiIi0x9zYHF09uiLsdhjCksOUDqfUYbFQRYQQCI4Nhoe1B5zLOSsdDhERERERERFRiQjwDAAALnSiABYLVeRKyhXEpcahhUsLpUMhIiIiIiIiIioxvhV9UalcJWyJ2oKs7CylwylVWCxUkbwhyJyvkIiIiIiIiIg0TKfTwd/TH/Fp8QiJC1E6nFKFxUIVOXLzCEx0Jmjs1FjpUIiIiIiIiIiISlSAV85Q5EgORX6eWCxUiczsTByNO4q6DnVhaWqpdDhERERERERERCXK3doddR3qYmfMTjzIfKB0OKUGi4UqEZoQigf6BxyCTERERERERESlRoBnANL0adgVs0vpUEoNFgtVgvMVEhEREREREVFp4+fhBxMjEwRGBiodSqnBYqFKBN8MhpWZFWrZ11I6FCIiIiIiIiKi58LWwhatK7VGSFwI4h/EKx1OqcBioQrcfXgX55LOoalTU5gYmSgdDhERERERERHRcxPgFYBskY0tUVuUDqVUYLFQBY7FHUO2yOYQZCIiIiIiIiIqddq6toWVmRUCozgU+XlgsVAFDt88DIDzFRIRERERERFR6WNmbAY/Dz9cvn0ZYclhSoejeSwWqkDwzWC4lnOFm5Wb0qEQERERERERET13AV4BAICgqCCFI9E+FgsN3LWUa7h+/zp7FRIRERERERFRqVXfoT5cy7lic9RmZGVnKR2OprFYaOCCY4MBAC1cWigcCRERERERERGRMnQ6Hfy9/JGQloCQ2BClw9E0FgsNXPDNYBjpjNDEuYnSoRARERERERERKcbf0x8AuNBJCWOx0IDps/UIiQ1BbfvasDazVjocIiIiIiIiIiLFuFu7o65DXeyK2YUHmQ+UDkezWCw0YOeTzuNe5j3OV0hEREREREREBKCHZw+k6dOwK2aX0qFoFouFBiz4ppyvkMVCIiIiIiIiIiKgq0dXmBiZIDCSQ5FLCouFBiz4ZjDKmpRFXYe6SodCRERERERERKQ4WwtbtKnUBiFxIbiVekvpcDSJxUIDlZqZitCEUDRxagJTI1OlwyEiIiIiIiIiMggBXgHIFtnYcmWL0qFoEouFBupY3DHohR7NXJopHQoRERERERERkcFo49oG1mbWXBW5hLBYaKAO3zwMgPMVEhERERERERE9yszYDF09uiL8djjCksOUDkdzClws7NKlC+rWrYv69eujdevWOHXqFAAgPDwcLVq0QNWqVdG4cWOcP38+72eKuo/kfIVOlk6oYl1F6VCIiIiIiIiIiAxKD68eAMCFTkpAgYuFa9asQWhoKE6fPo1Jkybh1VdfBQCMHj0ao0aNwuXLlzFt2rS8+59lX2kXez8W0SnRaO7cHDqdTulwiIiIiIiIiIgMSj2HenCzcsOWK1uQlZ2ldDiaUuBioa2tbd73d+/ehU6nQ3x8PI4fP47BgwcDAPr06YNr164hIiKiyPsICI4NBgC0cGmhcCRERERERERERIZHp9PB39MfCWkJCIkNUTocTSnUnIVDhw6Fm5sb3nvvPfz222+4du0anJ2dYWJiAkA2VOXKlRETE1PkfSSHIOugQ1PnpkqHQkRERERERERkkPw9/QEAm6I2KRyJtpgU5sG//vorAGDZsmWYNm0aPv744xIJ6lFz5szBnDlz8m6npKRougdifEI8Dl0/hCqWVZB0PQlJSFI6pGKRmJiodAglQot5aTEngHmpiRZzApiXmmgxJ4B5qYkWcwKYl5poMSeAeamNFvPSYk6A8nlVs6qGndE7cbbiWZQxLlNsv1fpvJRUqGJhrldeeQVjxoyBq6srYmNjodfrYWJiAiEEYmJiULlyZVhbWxdp399NmjQJkyZNyrvt6uoKb2/vomds4CLvR+Ke/h76V++vuTy1lk8uLealxZwA5qUmWswJYF5qosWcAOalJlrMCWBeaqLFnADmpTZazEuLOQHK5tVP3w+fhHyCKyZX8hY9KS5aba//UqBhyHfu3MHNmzfzbv/555+wt7dHxYoV0aBBAyxfvhwAsH79+rxiXlH3lXan75wGADR3aa5sIEREREREREREBs6vih9MjEy4KnIxKlDPwrt376Jfv35IS0uDkZERHBwcEBQUBJ1Oh4ULF+LVV1/FrFmzYG1tjSVLluT9XFH3lWZn7pyBhbEFfCv6Kh0KEREREREREZFBszG3QVvXttgdsxu3Um/B0dJR6ZBUr0DFQnd3dxw9evSJ+6pVq4bg4OBi3VdapenTcDHlIpq6NIWZsZnS4RARERERERERGbwAzwDsitmFLVe2YFjtYUqHo3qFWg2ZStaJWyegF3o0d+YQZCIiIiIiIiKigmjt2hrWZtbYFLkJQgilw1E9FgsNSPBN2dOS8xUSERERERERERWMmbEZ/Dz8EHEnAmG3w5QOR/VYLDQgrSq1QoBLAHxsfZQOhYiIiIiIiIhINQK8AgCAC50UAxYLDUhzl+YYXmU4dDqd0qEQEREREREREalGPYd6cLNyw5YrW6DP1isdjqqxWEhERERERERERKqm0+kQ4BmAxLREhMSGKB2OqrFYSEREREREREREqufv6Q8ACIziUORnwWIhERERERERERGpnpu1G+o71MfumN1IzUxVOhzVYrGQiIiIiIiIiIg0IcArAGn6NOy8ulPpUFSLxUIiIiIiIiIiItKErh5dYWpkyqHIz4DFQiIiIiIiIiIi0gQbcxu0dW2Lo7FHEZcap3Q4qsRiIRERERERERERaYa/lz8EBLZc2aJ0KKrEYiEREREREREREWlGm0ptYGNug8DIQAghlA5HdVgsJCIiIiIiIiIizTA1NoWfhx8i7kQg7HaY0uGoDouFRERERERERESkKf6e/gCATZGbFI5EfVgsJCIiIiIiIiIiTannUA+VrSpjS9QW6LP1SoejKiwWEhERERERERGRpuh0Ovh7+iMpPQlHYo8oHY6qsFhIRERERERERESa4+8lhyIHRgYqHIm6sFhIRERERERERESa42blBt+KvtgdsxupmalKh6MaLBYSEREREREREZEm+Xv6Iz0rHTuv7lQ6FNVgsZCIiIiIiIiIiDSpq0dXmBqZIjCKQ5ELisVCIiIiIiIiIiLSJBtzG7R1bYujsUcRlxqndDiqwGIhERERERERERFplr+XPwQENkdtVjoUVWCxkIiIiIiIiIiINKtNpTawMbdBUFQQhBBKh2PwWCwkIiIiIiIiIiLNMjU2hZ+HHyLuROBS8iWlwzF4LBYSEREREREREZGmBXgFAAAXOikAFguJiIiIiIiIiEjT6laoC3drd2yJ2gJ9tl7pcAwai4VERERERERERKRpOp0O3T27Iyk9CcE3g5UOx6CxWEhERERERERERJrn7+kPgEOR/wuLhUREREREREREpHluVm5oULEB9sTsQWpmqtLhGCwWC4mIiIiIiIiIqFTw9/JHelY6dlzdoXQoBovFQiIiIiIiIiIiKhW6uHeBqZEpgiKDlA7FYLFYSEREREREREREpYKNuQ3aubXD0bijiEuNUzocg8RiIRERERERERERlRr+nv4QENgctVnpUAwSi4VERERERERERFRqtK7UGrbmtgiMDIQQQulwDA6LhUREREREREREVGqYGpvCz8MPkXcjcTH5otLhGBwWC4mIiIiIiIiIqFQJ8AoAAARGBiocieFhsZCIiIiIiIiIiEqVOhXqwN3aHVuvbIU+W690OAaFxUIiIiIiIiIiIipVdDod/D39kZSehOCbwUqHY1BYLCQiIiIiIiIiolLH39MfABAYxaHIj2KxkIiIiIiIiIiISh1XK1c0qNgAu2N2437GfaXDMRgsFhIRERERERERUakU4BWAh1kPsePqDqVDMRgsFhIRERERERERUanUxaMLzIzMEBQVpHQoBoPFQiIiIiIiIiIiKpWszazR1q0tjsUdQ1xqnNLhGAQWC4mIiIiIiIiIqNQK8AyAgGDvwhwsFhIRERERERERUanVqlIr2JrbIigyCEIIpcNRHIuFRERERERERERUapkam8LPww+RdyNxMfmi0uEojsVCIiIiIiIiIiIq1Xp49QAABEYGKhyJ8lgsJCIiIiIiIiKiUq12hdrwsPbAlitboM/WKx2OolgsJCIiIiIiIiKiUk2n08Hf0x/J6ck4fPOw0uEoisVCIiIiIiIiIiIq9fy9/AEAQZGle1VkFguJiIiIiIiIiKjUq1SuEhpUbIDd13bjgf6B0uEohsVCIiIiIiIiIiIiAAFeAXiY9RDBScFKh6IYE6UDICIiIiIiIiIiMgRdPLrg5v2bqGlaU+lQFMOehURERERERERERACszazxZoM34VzGWelQFMNiIREREREREREREQFgsZCIiIiIiIiIiIhysFhIREREREREREREAFgsJCIiIiIiIiIiohwsFhIREREREREREREAFguJiIiIiIiIiIgoB4uFREREREREREREBIDFQiIiIiIiIiIiIsrBYiEREREREREREREBYLGQiIiIiIiIiIiIcrBYSERERERERERERABYLCQiIiIiIiIiIqIcLBYSERERERERERERABYLiYiIiIiIiIiIKAeLhURERERERERERASAxUIiIiIiIiIiIiLKwWIhERERERERERERAWCxkIiIiIiIiIiIiHKwWEhEREREREREREQAWCwkIiIiIiIiIiKiHCwWEhEREREREREREQAWC4mIiIiIiIiIiCgHi4VEREREREREREQEgMVCIiIiIiIiIiIiysFiIREREREREREREQFgsZCIiIiIiIiIiIhysFhIREREREREREREAApYLExPT8eLL76IqlWrol69eujcuTMiIiIAAPHx8fDz84OPjw9q166N/fv35/1cUfcRERERERERERHR81fgnoWjRo1CWFgYzpw5g549e2LEiBEAgOnTp6NZs2YIDw/HkiVL8PLLLyMzM/OZ9hEREREREREREdHzV6BioYWFBV544QXodDoAQLNmzRAdHQ0AWLNmDcaMGQMAaNy4MVxcXLBv375n2kdERERERERERETPX5HmLJw3bx569uyJpKQkZGZmwsnJKW+fh4cHYmJiiryPiIiIiIiIiIiIlGFS2B+YNWsWIiIisGvXLqSlpZVETI+ZM2cO5syZk3c7JSUlb75ELUpMTFQ6hBLBvNRDizkBzEtNtJgTwLzURIs5AcxLTbSYE8C81ESLOQHMSw2M05JQ/tzPKHd1J8rb1sKNm/2R5twU0GljbVYttdWjmJf2FKpY+NVXX2HDhg3YuXMnypYti7Jly8LExARxcXF5vQSjo6NRuXJl2NvbF2nf302aNAmTJk3Ku+3q6gpvb+8iJ6wGWs2PeamHFnMCmJeaaDEngHmpiRZzApiXmmgxJ4B5qYkWcwKYl8FKvwsc+hY48iOQmQrYuME+bj8Qtx+wqQz4DgLqDwJs3ZSO9Jmpvq2egnlpS4HL83PmzMHKlSuxY8cO2Nra5t3fr18/LFiwAABw7Ngx3LhxA23btn2mfURERERERESkcZlpwKF5wNy6wIGvABtXYMBy4K2ziO75J9B6CpCdCez9DJhbB/itN3D+D0D/UOnIiTStQD0Lr1+/jsmTJ8PT0xPt27cHAJibmyMkJASzZ8/GkCFD4OPjAzMzMyxfvhympqYAUOR9RERERERERKRRWZnAqeXAvtnAvVjAxg3oOguoNxAwMgYA6K3cAN/3gHb/AyJ3ASd/BS5vk9+XtQfqDgQaDAEq1lA4GSLtKVCx0NXVFUKIJ+5zdHTE9u3bi3UfEREREREREWlMdjZwfgOw51MgOQooWwHw+xxoNBwwMX/yzxibAFW7yu1+PHBmpSwcHvlebq6NAd8hQO3egLnV882HSKMKvcAJERERERm47Gwg8TJw7Qhw7ShwLQSVH6YD1ToD3h2BKm0ACxuloyQiotJCCCBiJ7BrJhB3FjCzAtq9AzR/vXAFvnIVgZYTgBZvAjFHgFO/yWHJgW8C2/4H1O4F+A4F3JoAOl3J5UOkcSwWEhEREaldxgPg5kn5welaiCwQpt/J31++CnQQwIklctMZy54Y3h0Br46AS/28YV9ERETFKiZEFgmvHgKMzYHm44FWkwBL+6L/Tp0OcG8uN7/PgXPrZW/DU8vlVqGaHKJcdyBQzqH4ciEqJVgspJJ3ZjUczv0FXHGVvRjMrQELW8DC+pHbOd+bleMVICLSHn0GjNOSgJSyQLY+Z8v62/eP3n7kfvH3x+n/4+dybv/rz2U/+W899nP/9beyAJGNShYOQHhTwKku4FwXcKgOGHMO4hKXEptTFAyRBcK4UNkuAGBkKot/bk2Bys3k13IVcTUiAt52RkDELiByD3Blv+x5uOdToEx5wLOdLBx6dQBsKimZHRERaUHcOWD3x3KeQZ0x0GAo0HaaXMSkOFlYA42GyS3unOxtGLoa2D4D2PkhUO0F+be9OvDCGFEBsVhIJevSZuCPUbABgPACPF5n9Hjx0Nzmb0XFv99+9HE5+5421wUR0fPwIFkOr4k7C9w6J78mhKFKdqbSkT2dzggwMsnf/n7byDj/e2NTwNRCfg/ALOEyEH8q/3cZm8mJxp3qAE71ZAHRsTZgXk6h5DQgOwuIv5DTa/CoLPDdicnfX8YO8Okih1y5NQNcfGUbPYmdJ9DEE2gyUk4uf+0oELlbThZ//k85lAuQRd/cwqFHS8C0TImnSUREGpF8BdgzCzi7FoAAar4IdJgBVPAp+b/tVBvoNhvoNBMI2yx7G17cJDfrSkD9QYDvIKC8R8nHQqRiLBZSyblzDfjzdaCMHWI6LkDlKt7AwxQgPQVIv5vz/d0n3H7k+7s35FeRVfC/a2z+hKJizu0n9mp8dF/OfUZGJfbfQkQakZ0N3L7yz8Jgyo3HH2fjBnh3wh1Ywdaugiy86YyfXowzMv7b7Ufu1/39cX///gm/96l/K+erzviZXvOuhIfD295E9myLDc3/GnsGwPKcR+kAey/Z+9CpjiwgOtXjsKCneXgPuH48v9fg9eNAxr38/RWqyoncc3sN2nsXrVe+saksBHq0BDq+B6QmAVF7ZPEwYlf+xPHG5oB7i5whyx2AijU5CoCIiP7pXhyw/0vgxFLZ292ro3x/cfF9/rGYWgC1+8jt9lXg9O9yePL+L+Tm2U6+l1b3f/oFNqJSjMVCKhlZemD9CDlf0kurkWHsJT8oFoUQQOaDJxcV/150/HsB8l4ckBAGZNwv3N80t35yIfHRImOZ8jA2rQrAu2h5EZF6ZDyQPbviQuXwlrizwK3zQGZq/mOMTIGK1eXJp2NtWRRzrAWUtQMAJEZEwNZbg68XOh1gV0VuNXvm338//p8FxPMb5JbLyjl/+LJTHfl9eY/SVYgSArh7Tc7ndC1E9hq8dR4Q2XK/iQVQqWF+r0G3JnnHVLGztAfq9JWbEPKYj9gli4dXD8tCIiDbzauD3DzbP9ucU0REpH5pt4FD3wJHfgT0aXJO3I4fAFVaKx2ZVN4daP+OHAIduQc49StwaQsQtVd2JKk3UBYOnWorHSmRwWCxkErG3lnyA0+zcUA1PyAioui/S6cDzCzlZu1ctN+RnfUvPRlT/r0AmRQvv8/K+Mev9TAyBa4OkqtxFbUYSkSGQwh5keHWuccLg0kRAET+48rYAa4NZXErtzBYoSpgYqZY6AanXEXAu5Pccj28l/9/GndGFhAjdwPhf+U/xtzmkd6HOUVEh2ramQcxK1Pmn9tr8NpR4N7N/P3lHGUvh9z5Bp3qKnNc6XSy2O1YC2j5piyYXz2cP2T59O9yg07Oj+jVQfYgcWuinbYiIqJ/l/EACFkAHJorPy851JA9Cau9YJgX/oyMAZ9OcktNBM6sksOUQxbIzaWBXBSldl/ZSYSeLDVJnivfOi+/xl9E+YrNAe9ZSkdGxYjFQip+kXuAA3Nkd/NOHyodjWRkLCdvL1O+6L8jM/3xQuLtaDzcNxcWJ5YCJ5bJHjWt3lKmmz0RFV5WJpAYntNLMGcocdw54EHiIw/SyTneavbI7/nmWBuwdjHMk2BDZ26Vv3JhLn0GkHAppzh7Nn8I89WD+Y8xNpfzIOYWEJ3rySKWmeXzz6Gw0m7LYcS5qxTfOCF7ywMAdHJIbzU/WRx0a2q4PSvNyuZ/wAKAu9fl+31kzmIpN78GDnwNmFkBVdoAXu3lsGU7T2XjVpPMdODOVSD5CqyuXgIq9AFs3ZSOiojon7IygZPLgH1fAvfjANvKQLcvgDr91LOAiGUFoMV4oPk44Poxmc+5P4CgicC2d4BavWThsHJzw3xffh6yMoHEy/lFwVvn5bny/bjHH2diAfubJwGvBnJ0AmkCi4VUvO7HAxtGyQ+EfZdoq5eNqYXcrBzlbddGuG5RD96m8fJq2oU/5ebZDmj5lvxaWt9YiAxN2p2cE5yz+cXB+IuP9xg2KSMLUDUC5DAUp7qykMOFOUqWiZksAjrXzb8vbz7Iv8+DePqRH9TJufryCog58yAqOSRWCCA56pFVikOAhIv5+00tAddGcjhx5aZApUZAGVvFwn0mNq7yQ1SDIbL3/s1T+XMdXt4mJ5UHgPJVZK9D746AR+vS3VNDCLkA0u1oeXwnX3n8+0d6mDoCwOEPAOf6sqdpDX+56AzPK4hISdnZwLn1wJ5P5OuXpQPQ7Uug4SvqXWRSp8uZ6qMJ4Pe5XOjr5K/AmRVys/eWQ5TrvZT/OVCL7sc/XhC8dV5ezH10gT5jcznljndHefE8dwRCVib0P7aCycbxcqTNo+d0pFosFlLxyc6WhcLUeFkotKuidEQlT6fLnxw+7hxwaJ58A43aK0/wW70F1OihnitsRGonhOyZk9tLMLcw+OjKsYCcc61K25zegjmFQTtPPlcNhZGRnNrB3kte2c9171ZO4fBMfgHx3Hq55bJy+VsBsa7s8VASRRb9QxlLbq/BayFAakL+fmtXoFbv/IVIHGsDxho89TIylkVQ10ZA27dlcT76QM58h7uA4z/LzcgEcG0CeOcMWXaur70FxbL0QMp1+SE6+YosBOZ9Hy1HJ/ydha08Z6rcVBZX7aogLikFTvfOApe3yg/lez6Rr1HV/eXm2lh7/3dEZLiEAMK3A7s+kgUlc2u5unHTsdq6qGpuBTQYKrf4i8DJ34DQVcDOD2Tu1brJwqF3J/W+n+sfyjn9H+0teOvc4+cvgDyH8eqQXxB0qgPYeT0179g2X8Ft5yhg1SBg1F7OZ6wBKj3CySAd+kZOft7wVaB2b6Wjef6cagN9Fss3zuD58orU2lflyX2LN+XVKK60RVR8MtNlr624R4YQ3zr3+IdxnbGc765O/5zCYM5mWUG5uKnorBwBq86AT+f8+9LvyhPdR3sgRuyUvdtyWdjkzH/4SAGxQtXCn+inJj7ea/DmKSDrodynM5bvA7V6y94JlZvJ3nelURlb2UO3RkB+b8vcwuGVA0DMYWD3J3LuT6/2snDo1aHo8xI/bw/v5/QIjP5nD8E7MXIF0Mfo5LHgXE8OM7erklcURHmPJ06Rcj8iAvAeL4eARR8ELm2W2+Fv5WZZEaj+AlA9QA771tJIDiIyLFcPAztnyvnoTSyAlhPkKKqSWmzLUFSsAfjNktNqhW2Rn+0ubQYuBcmLzvVfBnwHG+50G3nzcJ+XF85vnZdb4uXH36dMyshcq3XL7y1YsWah2/ehQx2g+xxg03hg7SvAkD/VW1AlACwWUnGJOQLs/lS+sPh9rnQ0yirvDrzwJdDmbeDoQuDoYiDoLWDvZ0CzsUCj4fKDKxEV3P0EWQi6dS6/MJh4GRBZ+Y8xt84vBuYuOuJQnUV6rbOwAdxbyC2X/qHsEfDoPIg3T8nebrlMLOR7Vt5iKrnzIJaV+7OzgaTwx3sNJj2yWJe5NeDRKr/XYKWG2updUVx0uvxeok1HyTkqr4XIwmHErsd7hlasmb/KsnsLwLSMMjELIYdjPdYr8JHvU+P/+TMmZWThz6eLLAQ+WhS0dSv68Dxj05yCans5H1jsKeBikPzAemKp3MytZQG9ur/8am5V1MyJiPLFhgK7P5Y9CnXGQMNhsve4tYvSkT1fJmZArRflducacHoFcGq5nKf3wNdyio0GQ+UFMqXet3IvoOcWBG+dk+fKacmPP862snyfyu0t6FhHvlcV18iaBkPkiItji4HtM4BupbwuoHIsFtKze5AMrHsNMDaTw4+VepE0NOUcZC/DlhPkAijB3wM7P5SLvzQaDjR7XdvzXhAVhRAwvXsFOHs6ZwhxTnHw/q3HH2frLq+APloYLKmhpqQ+JuZyhV6X+vn3ZWfLHm65qzDHnZXFxJsn8x+jMwLsfeBiYgvcvSwXJ8lV3gOoOyB/lWKH6hy2XhQmZkCV1nLr9KG8EBC1J6fn4W7ZMz94vizmurfMn++wuOfr02cAd689Yahwzvd5i9A8wtJBHgee7fJ7Beb2ECznWPKvP0ZGsihdqSHQ6QO5QNOlIFk8zC26GpvJ+Kp3l6uRlqtYsjERkfYkRQJ7ZgHn1snbtfsA7d+VF31KO1s3oN00oM1U4Mre/N6G0Qfkxcs6/WXBzLleyfx9IYCUG/nzcOcWB5PCAZGd/zhTS8CxJuDYM6coWFvefh4dVvw+A+IvACE/youx9V8u+b9JJYLFQno2QgAbx8n5eXrMlxOe0uPMreRKW01GAWfXyHkND80FjvwI1H9JDlHmmy+VdonhQOga4OwauN+Ozr8/dxVcn875KxE71lLvohCkHCMjoIK33Gr3kfflDtHJW0hFFhLLJJ+UJ/puTeU8cm5NASsnZePXqnIOQN3+chNCXiDILRxGH5A9ELe/C1hXyulh1wHwbF+w4VHpd59cDEyOluctj36wAuScijZusr3/PlS4vIfh9dir4AO0mii3lFi5qMzFIPl/F74dCHxL5lIjZ57D0jCXNBEVXUossG82cOo3OUzVuzPQ8b2SK3ypmZFRfk/4B8lA6GpZODy2WG5OdWVvwzr9in7OmvFAjpLIm1cwp8dg+p3HH1e+irw45FhbTofiWAuw9VBuXltjU6DfMmBRW/k+5FBNXuQi1WGxkJ5NyEI5h0OdfnLOBno6EzP5f1TvZfl/dvAbOXzo5K9yEZRWbwEuvkpHSfT83I+XPWFC1+T37irniDvVBsC2VifZW9Deh/OdUMnR6eQ8edbOQNWueXdHhofD28dHwcBKKZ0ufyqBVm8BGalA9CFZMIzcLYd9nVoOQAdUapBXOLS4dR1ICf5nUfDRnqG5zKwAOw/Z6/TRocLlPWShUK2vN9bOQOMRcku7I4uFl4KA8J1ynrHtM4CKtXIKh93lB1n2xCYiQBa7Ds0FQhYB+jR5kaHjB3IBR/pvZe3kVFNNxwA3TgInl8nz2y1T5GtvjR6ycOjR6smvu0LI+W7zFhzJKQ4mRQIQ+Y8zs8pZaKR2fm/BijUM70IWIC8EDvwd+MUPWDUYGL2PPd1VSKVnRGQQbp4GdrwnJ3X1/4YnnQVlZJR/sn71EHBwLnDhT7l5tpM9BKq05f8nadPD+3K4xtk1QOQeOeegWTlZRK/bH6jSBolRV2Dr7a10pFSa8fXXMJhZAlW7yA2QH6Yid8ueh1H7gBsngP1f4h/LyFg5Aw41/jlUuHwV+aFO6+1bxja/t2Zmmvy/uhQIhG2VvYb2zQZsKsvzkBr+gFsz9RZJiajoMlLlSKdD3wIP78oLCh3flxfPtP46WRJ0OsC1odz8PgPO/yk7hZyVI2dQvgrQYAgsjNyAO/tzhhKfk0N2H12cDzlz/dbskTOiJre3oMqm23HxBQK+Bf4YBawZCgzdxMW4VIZnBlQ0D+8B64bJ7/suMcwrGoZOp5NXmDxayTknDs2TV6Gi9soX15ZvyYlyOScWqV2WXh7XoatlT5fMB3K4n09n+WG2arf8RSWIiJ7GtjLQ8FW5Zellj+ToA0i4mwoHn8Y5PQTdOXfyo0zLANX85Jall4vLXAqSW8iPcitjJ4ewVe8uh3rz/49I2/QZcnTT/i/lgk3lPYDuXwG1+yo3dFVrzCwB30FyS7gsh3afWQns+ujxC1wWNvnzb+f1Fqwuf14L6g2QU70Ezwe2TZMdjEg1WCykwhMCCJooJ4r3m/34BPJUNE51gD4/yQVRDs+XbyhrXwHsvICWbwL1Xir6SopEShBCfpAPXSOL4KkJ8n7XJrJAWKsXYFlB2RiJSL2MTQC3JoBbE9yNiIADeyP/N2MTOazQoyXQdZa8UHlpsywcnl4uN1NLuaBMdX/Zo7NMeaWjJqLikp0FnF0rFy+5c1UuzNT9a8B3KHt8lSSHqkCXj2WvzcvbkHj5KCrUaCWLg9aV1NVbsCg6zZTvN8d/kVNgNBqmdERUQCwWUuGdWi7faKp1B5qOVjoabcm9std2GnB0IXB0ERA4Qb6pN3tdrqJsYa10lERPlxwFhK6VvQiTI+V99t5A45FA3X5y2gIiIlKWTidXqXSuC7T/n5zn8dJmuV0MBC5ukj3APVrJwmH17oC1i9JRE1FRCCGnIdj9sRzyamEj5yRsOlo7PdjUwNgUqBGAO6Y1UKE0XeAyNgH6LZULnmyZClSsKRePI4PHYiEVTvwl+SS3dgV6ztf+lRCllHOQvQxbTpDDBIK/B3Z+AByYAzQeDjQdC1g5Kh2lYUuJlfNZ3TgO3DgB5/RM4GZHwL0l4NIAMLVQOkLtSE0Ezv8hC4TXj8n7LB3kRM91+8v/b75WEBEZLrsqQIvxcrufAFzeKldWjtojp5HYMkWuZlm9O1A9QPaUISLDF30Q2DkTuH4UMCkj50ZvOYG9hun5KmsHDFwB/NwFWDMEGLWXF6BUgMVCKrjMNDlPYVYG0PcX+aSnkmVuBbR4A2gySg7nPDRPrqIc/ANQ/2W5z95L6SiVl5EqF9y5cRy4LouDSLmRv9+0LMpk6YHYYHnb2Ayo1AhwbwG4N5ervnHezcLJeCBX9Q5dI1cqzdYDpmWBOv2BugPkYj2cMJ+ISH3KOciVOxsMlXNUR+yUPQ4v/wXs+khu9j45i7X5ywtCnOeMyLDcPC2fq5G7ZC/hxiOANlMBKyelI6PSyqkO0PN7WU9YPRh4dQs7bxg4fpKjgts2XXZd7/g+uw4/bybmQIMhQP1BQNhmWTA8sQQ4uQyo2VMuhlJa5o7MzgISwh4vDMZfAER2zgN0QMUagFcHwLWR7AnhUANREZfhbXFXrkAdEwzEhAAxh4EDAHRGgHM9oHILWUCs3BywtFcyS8OUnQVc2ScLhBcDgYz7gM5YTohfd4CcIN+8nNJREhFRcTG3knPM1uolF0WI3p8/XPngN3Kzcs7pcdgd8Ggth9oRkTISI4A9n8gRH9DJi7jt/8dpYMgw1O4t5y88OAfYMhnowZGKhozFQiqYc+vlcFjP9kDLiUpHU3oZGckVkqv7y2EFh+bKk4Hzf8i2aTURqNJGWy+6KbF5Q4lx/Thw85QsUuWycpZFqkoNZXHQxffJvQSNzWQvQvfm8naWHrh1FrgaLAuIVw/L333ke7nfoXpOz8OWsnhoU6nkczVEQgCxZ/IXKrkfJ+93aSALhLV7A+UqKhsjERGVPBMzwLuT3F74Wr4vXwqUw5WP/SQ3CxvAp6vsdejVkReQiJ4T49RbwKZv5dzyIguo6gd0eA9wqq10aESP6zBDFgxPLQec6gFNRykdET0Fi4X035KjgE0TAMuKQO9FHGpiCHQ6oEprucWGyuHJ5zfIuYVcfGXRsLo/YGSsdKSFU4DhxHDxzS8MVmpU9CKesYn8XS6+QPPXZVEsIUz2Nryasx3/RW4AYOsuC4fuzeVXO09tFWX/7vZVuZBR6BogMUzeV95DLr5Tpz9QoRRNzExERI8zMgLcGsut00z5/nkpUPY4PLtGbiYW8kJm9e5AtW6AZQWloyZSN30GkJoA3L8F3I/P/3o7Gu6ha4DsDDlKptMHQOVmSkdL9GRGxkCfn4DFHeTIxYo15GdaMjgsFtK/02cA64bLnlwDfmMPIkPkXBfo+7O8ShM8X16lWTNUrkDb4k2g3kA5jNnQFHo4cSPZ26+k5sHT6YCK1eXWaLgsHt6JySkc5gxdPrNCbgBQzjFnyHLO0OWKNdVfSH+QDFz4UxYIY3Lmdyxjl7OS8QDZDloukBIRUeE9+v7ZZipw9zpwaYssHoZvl4ul6IyAys1h49AMcBgO2LgqHTWRYcjOBtJu5xT+Hi0C/q0geP8WkJb81F+TWb4qzF+YJXv+8lyNDF0ZW7ngyU8dgbWvAKP2AbZuSkdFf8NiIf27nR/KoZmtJ8t5ychw2VUBun8NtJ0OhCwAji0GAt8E9sySPecaDgMsrJWLr6DDiXMLgy71lV10RKcDyrvLrf5L8r57t3J6HgbLIuL5P3PmhIEcelW5ef7QZed66pi3KTMduLxNFgjDtwPZmXK1vNp9ZIHQq4M68iAiIsNg4yqHlTUdJS9CXf4LuBQEROyCw9VDwPGvAdfGcs7lGj3k+yyR1jy8/+SC39/vS42Xi8Q9jbm17KxRsYb8Ws7xka8531tWxLVb9+Ht4/P88iN6VhWrA70WAqsHAateBob/BZiVVToqegSLhfR0Ydvk/G1uzYB27ygdDRVUOQeg43tAywlynskjPwA73gf2fw00fg1oNrbke4j+53BiSzn817WhHFL8LMOJnycrx/yJ3gEg7Q5wLSR/zsOInbLwBsgh066N84cuV2pkOG+A2dnA1YNA6GrgwibgYYrs9VGlrSwQ1vDn6tBERPTsytrJC271XwIyHiD24HI43z4qzzGvHwO2z5DnAzV6yOKhvZfSERMA3L6KctF/AQ/PyuHkJhaAaRk5UsUk5+vfb5eG3mxPGwb8pCJgZurTf4+xmSz0WTvLi+P/UgQs8LljfESxpEj0XNXwlx1d9n0OBE6QU56VhtcSlWCxkJ7s7g3gzzGAha2cU6Ckhn5SybGwBlq+CTQdLYtCh76VK08Ffw/4DgJavFE8K6P913BinRHgUAPw7phfGCzJ4cTPUxlboGpXuQGySHr9eP7Q5WtH5erBAGBkClRqkNP7sKVcUdzC5vnGG3dOHgtn1wH3bsr7nOvlLFTSB7Byer7xEBFR6WFWFqmVOwAdRsle7VF7gAsb5ZDlXTPl5lhHFg1r9gQcqiodcemhz5AjJ8J3yC0xDIU+I8gtKppYAKYWT7j9lCLjo7ef+HP/8nuMzZ69sFBMw4ABnZyXs5yjPMd7UvEv96uFLQsiRLnaTpMLnpxdIz+XtBivdESUQwOf1qnYZemB9SPkG+fAlZw/QO1MzIEGQ4H6g4GwzcDBb+SiHSeWAjVfBFq9JV+YCyp3OHFuYdDQhxM/T2aWgGdbuQHy5Dv2dE7Pw2Ag5ojsiXhoLgCdXKHOvWX+3IflHIo/prvXcxYqWQvEn5f32VYGWk8B6vYHHKoV/98kIiL6N6YWctGTat3ke+WVfXLO3EubgT2fyM2hRn7hsGINFleK293r+cXBK/vyz+XKOQG+QxBXtiqcnF0B/UNAnyYLvPpHtkdvZ6blP07/MP92WnLO43L2ZWUUcxK6nEJiwYuODneSgZD04h0GXM4RKFtBGxfCiZ43IyOg1wLgp07AjvcAx5pyGiRSHF/R6J/2zZZXF5uOAaq/oHQ0VFyMjIAaAXKV5OgDwMG5cgXl8xvkC3KriYDH31aiykiVxcDceQb/dThxI1kgtHZ5rmkZNBMzwK2J3FpNlL0wb52Xi4fkDl0OWSA3ALD3yZ/z0L25LOoVRdod2VsjdI38OxDyKnaj4bIXoVtTfugiIiLDYGIG+HSWm/9ceY5yYSNwMVAOTdv3uXx/zC0cOtXhe1hRZGXKC5bh22WBMP6CvF9nJM8LfDoDPl0Ax9qATof7ERGAt3fxxpCd/beC41OKjEUtTub+XGrS4/uyM/NCsAFKZhgwERWdhbVc8GRxB2DtMGDUXjkfPymKxUJ6XNQ+YP+XsqdZ54+UjoZKgk4HVGkjt9gzwKF5cpGOyN2ASwPYOLcDLiT9x3DinMKgQ3XAyFjRdFTFyFiuXu1cVw4PFwJIiswvHMYcBk4ukxsA2Ljl9DrMGbpcwefpH5D0D+UHgNDVcjL5rAzA2Fx+sKrbH/DuLD+QERERGSpjU3kB06sD8MLX8n0xt3B44Cu5la+SXzh08WXh8N+kxMr5lMO3A1F75RzFAGDpANR7WRYIvdoDZco/n3iMjGTx7XkX4LKz8gqOUdFX4VmTxw2RwangLac/W9EfWDUIeG07YF5O6ahKNRYLKd/9BGDDSDmUsu8S2ZWftM25HtD3F6DDDODwfODUcjjcPCn3WbkA1bvLwmClhvKEnC/YxUunk2+MFbyBhq/I++5cy+l5eFhuoavlBsghLu7N84cuV6wFi1sngQvfyuFb6XcB6IAqrYE6/YGaPZ7/vIhERETFwdgk/+Jmty9kr7gLG+XCXIfmys2msnyvq/miPFcxMlI4aIVl6eVUMeHb5RZ3NmeHTl7k9ekiC4RO9UrX/5WRsfx8Y2aJbPPbLBQSGaqqXeRCnbs+Aja+DvRbxuerglgsJCk7G/hjtJy/o8/PXI2utLHzBPznAO2m4+aJrXDx7czhxEqxdZNb3f7y9v0EWTzMHbp8abPsYQEARiZwzZ1nx7EO0LofULuvOlaWJiIiKigj45xpOloAXT+TBbELG+UWPF9u1pXyV1V2a1p6imH344GIXbI4GLkr58IhgLL28sKhTxfZU9PSXtk4iYgKotUkIDZUdoQ4OAdoPVnpiEotFgtJOjxPnmD4DgHq9FU6GlJKuYp4UKklC4WGpJxDTq+JHvJ2eopcZfnqISD2NG5buKF8m9GAYy1l4yQiInoejIzy5wPu8glw82R+4TDkR7mVc5LzNNfsKQuMWpoyJTsLuHESiNghC4Q3T+Xvc2mQP/egi6+28iai0kGnA178AUgMB3Z9LDtEVO2idFSlEouFJAsPuz6W8891+0LpaIjo31hYAz6d5AYgKSIC5R2LeQJyIiIiNdDp5PDjSg2BTjOBuND8wuGxxXKzdJCLu9XsCXi0kvMiqk1qkryoH75DzkGYlizvt7AFaveR8xJ7d5SLchARqZ2ZJTDwd2Bxe2D9CGDkbjltEz1XLBaWdmm3gXXD5YlT3yVc8YuIiIiI1Eenk3MxO9cDOrwHxF/MKRz+CZxYIrcydkD1F+Qch1XaGu7CX9nZQOzpnOLgDuD6cQBC7nOqCzQaLnsQVmok53YkItIauyqyPrG8N7DqJWDELtlpgp4bvruUZkIAG8cDd68BAfMAx5pKR0RERERE9Gx0Onle61gTaP8/ICFMLoxyYSNwarnczG1yCoc9Ac/2gKmFsjGn3QYidwPhO2WBMDVB3m9uLaci8ekCeHcCrJyUjZOI6Hnxag90/hjY/q5cX2HA76VnPloDwGJhaXbsJ+BSEFCrN9DgFaWjISIiIiIqfg7VgLZT5ZYUmT9U+cxKuZlZAdX8ZOHQuxNgWqbkYxJCrlYcvl0OLb4WAohsua9iLaD+IFkgdGuizqHTRETFofk4OcVE6Gpg32x5AYieCxYLS6vYUOCvd4DyHrJXIZckJyIiIiKts/cCWk+S2+3o/B6HZ9fKzdRSTqZfs6cs1plZFt/fTr8LRO2VBcLwncD9OHm/WTmg2gtyaLF3J8DGtfj+JhGRmul0sl6RcAnY9zngVAeo4a90VKUCi4Wl0cP7wLph8opm3yUc+09EREREpU95D6Dlm3K7cw24GCgLh+f/kJtJGbmgWM0XZeGwsOfMQsi5E8O3y/kHrx0BsvVyX4VqQJ2+8vdWbm648ycSESnNtIwcgryonRyObL8LqFhd6ag0j8XC0mjzZCApAug6C6jUQOloiIiIiIiUZesGNH9dbik3gYtBsnB4MUgWEY3N5YrDNXsCVf2AMrZP/j0P7wNX9uUXCFNuyPtNy8pegz6d5erF5d2fW2pERKpn6wb0/xX4tYdc8GTkbqBMeaWj0jQWC0ub0yuA0FXyJKfZ60pHQ0RERERkWKxdgKaj5Hbvlpzj+8JG4PI2IGwLYGQqJ96v2ROo9gJM714BDm+TBcKrh4HsTPl77L2BpmNlgdC9pfKLqBARqZlHS8Dvc2DLFGD9CODlNYCRsdJRaRaLhaVJwmXZq9DKBej5A+cpJCIiIiL6N1aOQOPX5JaaCFzaLAuHkbtlcRBAXh9BEwvAs13+3IP2XkpFTUSkTY1HALGn5ar2uz8GOn2odESaxWJhaZGZBqx9FdCnA31/BiztlY6IiIiIiEg9LCsADV+R24NkIGwrEP4X7ujNYduoH+DRCjArq3SURETapdMB3ecA8ZeAg98ATnWB2r2VjkqTjJQOgJ6Tv94B4s8D7d4B3FsoHQ0RERERkXqVtQN8BwH9f0Vik2lyBWUWComISp6JOTBgOVDOEdg4Dog7q3REmsRiYWlw/g/g+C9AlTZA60lKR0NEREREREREVDTWzrJgmJUJrHpZ9vamYsViodbdjgY2vQmUrQD0XswJQImIiIiIiIhI3dyaAN2/Bu7EAGtfAbL0SkekKSwWapk+A1g3HHiYAvReCFg5KR0REREREREREdGza/gK0Og14Mp+YMf7SkejKSwWatnuj4AbJ4CWb8kV2YiIiIiIiIiItMLvc6Byc+DI98CZVUpHoxksFmpV+A7g8HeAaxOgwwyloyEiIiIiIiIiKl4mZkD/XwHrSnIKthsnlY5IE1gs1KKUm8AfowELG6Dvz4CxqdIREREREREREREVv3IV5YInALB6MHA/Xtl4NIDFQq3JzgI2jAIeJAE95gO2lZWOiIiIiIiIiIio5FRqAATMA1JuAGtekSslU5GxWKg1+78Eog8AjUcCNXsoHQ0RERERERERUcmr/xLQ7HUg5jCwbbrS0agai4VacuUAsG824FQH6PKJ0tEQERERERERET0/nT8GqrQBjv0EnFimdDSqxWKhVqQmAutHACZlgL5LAVMLpSMiIiIiIiIiInp+jE1kTcS2MrBlCnDtqNIRqRKLhVqQnQ38MQa4Hwf4zwEqeCsdERERERERERHR82dpDwxcAeiMgdVDgJRYpSNSHRYLtSB4PhCxA6g/CKg3UOloiIiIiIiIiIiU41QHePF72alqzRBA/1DpiFSFxUK1u34c2DUTqFAVeOFLpaMhIiIiIiIiIlJe7T5Ay7eA68eAzZMBIZSOSDVYLFSztDvAumGya23fJYCZpdIREREREREREREZho7vA14dgVO/yUVPqEBYLFQrIYDAN4E7MYDfZ4BTbaUjIiIiIiIiIiIyHEbGQN+fgfJVgG3TgehDSkekCiwWqtXxX4ALG4GaPYFGw5WOhoiIiIiIiIjI8JQpD7y0EjCxANYMBe5eVzoig8dioRrFnQO2/U8uBR7wLaDTKR0REREREREREZFhqlgD6LUAeJAIrBoEZKYpHZFBY7FQbTJS5TyFIgvouxQoY6t0REREREREREREhq1GANB2GhB7GgicwAVP/gWLhWqzZSqQeBno+AHg2lDpaIiIiIiIiIiI1KHtdKBqNyB0NXDkR6WjMVgsFqrJmVXA6d8B785A8/FKR0NEREREREREpB5GRkDvRUCFqsD2GUDUXqUjMkgsFqpFYjgQNAmwcpbj7I3YdEREREREREREhWJhDQxcAZhZAmtfBW5HKx2RwWHFSQ0y04G1wwB9GtB7MWBZQemIiIiIiIiIiIjUqYIP0OcnIO2OXPAkI1XpiAwKi4VqsH0GcOss0OZtoEprpaMhIiIiIiIiIlK3ql2BDjOAW+eAjeO44MkjWCw0dBc2AccWAx6tgbZvKx0NEREREREREZE2tJ4M1OwJnP8DODRX6WgMBouFhuz2VWDTeKCsvRx+bGSsdERERERERERERNqg0wE9fwAq1gR2zgTCdyodkUFgsdBQZWUC618D0u8CvRYC1s5KR0REREREREREpC3m5YCBvwMWNsC64UBSpNIRKY7FQkO1+xPg+jGgxRuAT2eloyEiIiIiIiIi0iY7T6DfEiDjHrDyJeDhPaUjUhSLhYYoYqccK1+pIdDhfaWjISIiIiIiIiLSNq8OQOePgMQw4I8xgMhWOiLFsFhoYIwfJAAbRgPmNkDfXwATM6VDIiIiIiIiIiLSvubjgTr9gEtBsLm0UuloFGOidAD0iOwsOB5+D3iQCPRbBpT3UDoiIiIiIiIiIqLSQacDAr4Fytjhnnt3OCgdj0LYs9CQHPgaZeOOAY2GA7VeVDoaIiIiIiIiIqLSxaws8MIXyDa3VToSxbBYaEjKeyDNoR7QdZbSkRARERERERERUSnEYciGpG5/3CjjC2/TMkpHQkREREREREREpRB7FhoanU7pCIiIiIiIiIiIqJRisZCIiIiIiIiIiIgAsFhIREREREREREREOQpULHzzzTfh4eEBnU6H06dP590fHh6OFi1aoGrVqmjcuDHOnz//zPuIiIiIiIiIiIhIGQUqFvbt2xcHDx6Eu7v7Y/ePHj0ao0aNwuXLlzFt2jS8+uqrz7yPiIiIiIiIiIiIlFGgYmGbNm3g6ur62H3x8fE4fvw4Bg8eDADo06cPrl27hoiIiCLvIyIiIiIiIiIiIuWYFPUHr127BmdnZ5iYyF+h0+lQuXJlxMTEwMbGpkj7vL29//F35syZgzlz5uTdTklJ0XRhMTExUekQSgTzUg8t5gQwLzXRYk4A81ITLeYEMC810WJOAPNSEy3mBDAvtdFiXlrMCWBeWlTkYuHzMmnSJEyaNCnvtqur6xOLilqi1fyYl3poMSeAeamJFnMCmJeaaDEngHmpiRZzApiXmmgxJ4B5qY0W89JiTgDz0poiFwvd3NwQGxsLvV4PExMTCCEQExODypUrw9raukj7iIiIiIiIiIiISDkFmrPwSSpWrIgGDRpg+fLlAID169fn9for6j4iIiIiIiIiIiJSToF6Fo4ePRqbN29GXFwcunbtCisrK0RERGDhwoV49dVXMWvWLFhbW2PJkiV5P1PUfURERERERERERKSMAhULFy5c+MT7q1WrhuDg4GLdR0RERERERERERMoo8jBkIiIiIiIiIiIi0hYWC4mIiIiIiIiIiAgAi4VERERERERERESUg8VCIiIiIiIiIiIiAsBiIREREREREREREeVgsZCIiIiIiIiIiIgAsFhIREREREREREREOVgsJCIiIiIiIiIiIgCATgghlA6iMMzNzeHg4KB0GCXm/v37KFeunNJhFDvmpR5azAlgXmqixZwA5qUmWswJYF5qosWcAOalJlrMCWBeaqPFvLSYE8C81CohIQEPHz584j7VFQu1ztXVFdevX1c6jGLHvNRDizkBzEtNtJgTwLzURIs5AcxLTbSYE8C81ESLOQHMS220mJcWcwKYlxZxGDIREREREREREREBYLGQiIiIiIiIiIiIcrBYaGAmTZqkdAglgnmphxZzApiXmmgxJ4B5qYkWcwKYl5poMSeAeamJFnMCmJfaaDEvLeYEMC8t4pyFREREREREREREBIA9C4mIiIiIiIiIiCgHi4VEREREREREREQEgMVCIiIiIiIiIiIiysFiIREREREREREREQFgsZCIiIiIiEgxXG9SPdhWpDStHoPMy/CwWKgBYWFhiIiIUDqMYhcTE4Pk5GSlwyh2WmwvtpW6aLG9tJgToN1jUIt5aTEnQLvPLa3mpcXjUIs5AcDp06cREhICANDpdKr+QPkoLbYX20pdtJiXVo9B5mXYWCxUuY0bN6JRo0ZYsGABzp8/r3Q4xebPP/9E7dq1sWzZMiQkJCgdTrHRYnuxrdRFi+2lxZwA7R6DWsxLizkB2n1uaTUvLR6HWswJAAIDA9GqVSt888032Lt3LwB1f6DMpcX2Ylupixbz0uoxyLwMn06oMWoCAFy7dg2vv/46vL298eDBAzg4OODll19GzZo1lQ7tmYSHh2PUqFGoWrUqYmJi4O/vj/79+8PBwUHp0J6JFtuLbaUuWmwvLeYEaPcY1GJeWswJ0O5zS6t5afE41GJOABAfH48333wT3t7esLCwQHR0NAYPHox27doBkEPWdDqdskEWgRbbi22lLlrMS6vHIPNSCUGqlZCQIHbs2CGEEGL37t1i4MCBYsaMGSI0NDTvMdnZ2UqFV2TXr18XGzZsEEIIsXbtWtG6dWsxf/58ERsb+9jj1JabFtuLbaUuWmwvLeYkhHaPQS3mpcWchNDuc0ureWnxONRiTkIIkZKSIkJCQoQQQpw5c0ZMnjxZvPbaa2LXrl15j3n48KFS4RWZFtuLbaUuWsxLq8cg81IH9ixUoczMTACAqakpMjMzYWpqCgDYs2cPFi1aBC8vL0yZMgX79++Hk5MTmjRpomS4hZKdnQ0jIyOkpaWhTJkyAIB169bh22+/xYABAzBu3DgEBgaiQYMGqFSpksLRFoxW24ttpZ62ArTZXlrMSavHoBbz0mJOubT43AK0mZcWj0Mt5gQA9+7dg5mZGczNzR+7PzQ0FL/++ivu3LmDqVOnIiwsDHq9Hr169VJFDxQtthfbSj1tBWgzL60eg8xLXXmxZ6HK/Pnnn2LQoEGiY8eOIigoSERERDy2f/fu3WLUqFGiQ4cOwsrKSly6dEmhSAsnMDBQjBs3TvTp00eEhISIO3fuPLZ/7dq1ws/PT/Tp00dYWlqKsLAwhSItHC22F9tKPW0lhDbbS4s5CaHdY1CLeWkxJyG0+9zSal5aPA61mJMQQmzYsEH06NFDtGrVSixcuDCv50muM2fOiI8//lj4+voKW1tbceHCBYUiLRwtthfbSj1tJYQ289LqMci81JWXEEKwWKgiBw8eFF5eXmL37t3i888/FwMGDBBTpkwRJ0+efOxxY8eOFc7OzuLcuXMKRVo4u3fvFj4+PmLDhg3i7bffFq1atRJz584V0dHRjz1uyJAhwtHRUTV5abG92FbqaSshtNleWsxJCO0eg1rMS4s5CaHd55ZW89LicajFnIQQ4tixY8LT01McPXpU/PLLL2LixInipZdeyhsumWvKlCmiUqVKqslLi+3FtlJPWwmhzby0egwyL3XllctE6Z6NVHAnT55Ely5d0L59e7Rv3x779+/H2rVrsXLlStjZ2cHd3R0XL17E+fPnsWXLFtSqVUvpkAtk//79GDhwIHr16oVevXphw4YNWLlyJXQ6HYYPH45y5crh2LFjuHTpEv766y/V5KXF9mJbqaetAG22lxZzAoATJ05o8hjU4nNLizkB2n1uaTUvLR6HWswJAMLCwtC4ceO87eLFi1i/fj2WLFkCe3t7+Pr64vr16zh79iwCAwNVk5cW24ttpZ62ArR57nTp0iVNHoNafW5pNa9cRkoHQP9N5Ewr6erqitTUVDx48AAA0KZNG/Tr1w8RERGIiIgAADg5OWHDhg2oX7++UuEWmr29PZKTk/Nu9+7dGwMHDsTKlSsRGRkJQOa+ceNG1KtXT6kwC0zL7WVnZ6eptsqlxbYCtPfcArR3DGZnZwMA3NzcNHkMurm54f79+5rI69HXdq3kBOTnpbXnVi6tvQ5q+RxDq6+DPj4+yMjIwOXLlwEANWrUQO/evWFra4uzZ88CACpUqIC1a9fC19dXyVALRWuvhYB220qrz63KlStrJq/09HQAQLVq1TR5DFatWhUPHz5kXirDYqEK5J4Y1qlTB0ePHsV3332Xt69NmzaoV68efvzxR2RlZaF8+fKwt7dXKtQi6dixI5YuXYrFixcDkPn26dMHTZs2xffff4/s7Gw4OzvD2dlZ4UgLRmvtlZ2dnTdxcNu2bTXTVrGxsUhLSwMA1KtXTxNtBQBXrlzBvXv3AGjnuXX48GFcv34dANCpUydN5AQAO3bswB9//AFAO68XAJCUlISUlBQAQP369XHs2DHV55WVlYWsrCwAgK+vL06cOIF58+bl7VdjToB8/uQWrDt27Ihly5Zh4cKFefvU+tx6lFZeB3Np7RzjUVp5vQAAvV6fd+5UuXJlpKenY/ny5bhz5w4AoGbNmqhduzZWrVoFvV4PCwsLWFlZKRhxwaSlpT127qSF9oqKisprFzc3N8201YULF/IulGjpubVnzx6EhYUBkO/HWshr586d+PHHHwEAlSpV0swxeO3aNSQkJODhw4fw8vJCRkaGJvJKS0vLK+56enoiMzMzbxETQL15PQmHIRuw7du3IzAwEPHx8Zg6dSoaNWqEX375Bd26dQMAjB49Gra2tnB1dUVsbKw6VtQBcPnyZVhYWKBy5coAZAV+3bp16Nu3L4QQGDVqFADA3d0dcXFxMDJSR01bi+21ZcsWrF+/HteuXcPMmTPRvHlzrF27Fv369UN2djZGjx4NQH1tdfDgQQwcOBDvvfce+vfvD09PTyxduhRdunQBoM62AmR7/fDDD/jxxx9Rrlw5TTy3Nm/ejClTpmDOnDlwcXFBjRo1NHEM7ty5Ey+++CKsrKzQsmVLeHt7Y9myZao/BoOCgvDNN9/g7t27mDhxIgYNGoSlS5eia9euEEJgzJgxqstr8+bNWLt2LW7duoVRo0ahV69eWLlyJdq2bQudTqfKnAAgMDAQ69evR3x8PMaNG4fu3btj06ZN6NGjR15bAep7bvEcQz3HYWhoKB4+fIjGjRsDADw8PLBs2TJ07twZgDpzAoCtW7di9erViI6OxrRp09CtWzfMmjULw4YNAwD069cPderUga2tLaytrZGVlQUTE8P/SLZ582YsW7YM169fx/Tp09GjRw/VnzuFhISgS5cuGDNmDCZNmgRnZ2d89tlneOWVVwCot622bduGzz//HIsXL4adnR08PDywdOlS1T+3tmzZgnHjxuHbb7+Fj48P3N3d8csvv8DPz0+15xg7d+5Ev379cO/ePfTu3Rvu7u6YPXs2hg4dCkC9x2BQUBA+/fRTGBsbo0ePHnj77bfznltCCPTv31+VeQUGBmLp0qVITEzEoEGD0L17d3z33Xfo27cvAGDAgAGqzOupnucEiVRwQUFBon79+mLJkiVi6tSpwtbWVoSHhwshhDh69KioV6+eGDhwoBg4cKCoWrWqOH36tMIRF8zGjRuFTqcTU6ZMEVFRUY/t++uvv4S1tbUYNWqUGD16tKhevboIDQ1VKNLC0WJ7bd26Vfj6+oqgoCAxadIk0bhxY5Genp63z9raWowcOVJ1bSWEEHv27BEeHh5iwoQJYvHixSIxMVEIISeprV+/vhgwYICq2kqI/Pbau3fvP/Zt3rxZle119uxZUa1aNREcHCyEEEKv14vMzEwhhBA7d+4UNjY2YsSIEarKSQj5Wufr6yvWrl0rRo8eLTZs2JC3T62vF0LIvBo0aCCOHDkiFi9eLJo2bSru3bsnhBDixIkTqnxu/fXXX6J+/fpix44dYtGiRaJatWpi1KhR4u7duyI0NFS1bbVnzx5RtWpVERQUJL788kvRokUL8fHHH4tbt26JvXv3ChsbG9W9XgjBcww1HYeBgYHCyMhIDBs2TBw4cOCxfWrNSYj8tgoKChKzZ88WdnZ2ebGfPXtWvPjii8Lf31/4+/sLb29vcerUKWUDLqAtW7YIX19fsW/fPvHFF1+ImjVrirt37woh8ttLba/vQghx6dIl0bBhQzF58mTxv//9T1y/fl0IIcT58+dF7969Rffu3VXZVvXr13/seZWVlSWEEOLIkSOqbavw8HBRs2ZNcfjwYSGEEOnp6SItLU0IIVebrV+/vujfv7+q8tq2bZvw9fUV+/fvF5MnTxZff/11Xlup+fVi165dwtfXV5w4cUIEBQWJ5s2bi6SkJCGEEJcvXxa9evUSL7zwgury2r17t6hfv74ICQkRa9euFR07dhTdu3cXYWFh4sqVK6JXr16qfM34NzohcsYzkMG4ePEiXn75Zfz4449o1qwZAGDgwIEYPnx43tW7a9euISwsDFevXkW7du3g5eWlZMgFEhMTg3HjxsHX1xdnzpxB3bp1MXz4cFSpUiXvMZcvX0ZISAji4uLQo0cPVKtWTcGIC0aL7RUaGoqRI0fiiy++QNu2bXHkyBF89tlnaNeuHRo3boxWrVrh0qVLOHr0KG7duqWatsoVHh6O999/H7Vr18bp06fx0ksvwcnJCZUrV4ZOp8PFixcRExODtm3bGnxbAbK9unXrhg8++ACjRo1CfHw8goKCcOvWLfj5+cHX1xcXLlzA8ePHVdVeu3fvxuLFi7Fy5UpcuXIFc+fOxfXr19GxY0e89tpruHHjBg4dOqSq14sDBw7g1VdfxW+//YYWLVpgzJgxCA8Px65du/Iek/t6oaZjEAAmTpyIOnXqYPjw4bh27RoGDRqEGjVqwNfXF4MHD8a9e/dw/vx5VeU1ZcoU1KtXD0OGDAEAvPfee1i/fj369u2Ljz76CNHR0YiIiFBVTgAwZ84cxMXF4YsvvgAgezYsXrwYdevWxbvvvouLFy/i2LFjqnq94DmGes4xbt26hcmTJ6Nq1aq4ffs29Ho9BgwYgFatWuU9JiYmBpcvX1ZNToCcBuTVV1/Fxx9/jDZt2gAARo4ciebNm2P48OEA5DQNsbGxiIiIgK+vL9zd3ZUMuUDCw8MxZswYvPfee2jXrh3CwsIwZswYNGvWDM2aNUPPnj1x48YN1Z07AfK5M3r0aAQEBCA4OBi1atVC/fr14ejoCA8PD1y/fl11beXv74+XXnoJH374IRISEvD777/jypUr6N69O7p06YKrV68iPDxcdW114sQJfPDBBwgKCkJMTAzef/99JCYmon79+hg+fDisrKxw5swZ1eR1/PhxjBgxAt999x1at26NWbNmYevWrThw4EDeY3JfLyIjI1G/fn1VHIMA8OGHH8LCwgLTp09HYmIiunbtiho1asDb2xsvvfQS3NzccOXKFURERKgqr48++ghly5bFlClTAADz58/HkiVL0LBhQ3zxxRfQ6/WIi4tTXXv9K6WrlfRPt2/fFgsWLBBZWVlCr9cLIYTo06ePmD17tsKRPZv79++LzZs3CyGEOH78uGjfvr2YMWOGiIiIUDiyZ5OcnCx++OEHodfrNdNeycnJIiQkRAghRFxcnKhbt64YOXKk+OSTT0T16tXFn3/+qXCERZeZmSmio6PFq6++KtLT08WSJUtE+/bthZOT0z+WuVeLW7duiS5duoj3339fbNu2TTRp0kSMHz9eDB06VFhaWoojR44oHWKhZGdnCyGECA4OFgMHDhRpaWmiQ4cOYt68eeLHH38UPXr0EF999ZXCURZNXFycOHr0aN7t27dvi8aNG4t169YJIfKv/qtNVlaWGDFihBg0aJBYt26dqF69upgxY4ZYuHCh6NGjh/jiiy+UDrHQsrKyRP/+/cWkSZPy7ps5c6YYO3asqF69+j96Q6nJ4sWLxdChQx+7b8eOHaJx48aqfR28f/++2LJlixBCW+cYWjwnTEtLE/v37xdCyJ5dw4cPF2+88YbYt2+fwpE9m5SUFPHrr7+KjIyMvF7wo0aNEm+//bbCkT2blJQUcebMGSGEEPHx8aJBgwbizTffFN9//73o1KmTWLBggcIRFo1erxcpKSli3Lhx4u7du+LgwYPCz89PlC9fPu89WW2Sk5PF2LFjxZQpU/J6+L/zzjtiwoQJolq1aiIwMFDpEIvsypUrom/fviI8PFx06dJFLFiwQKxfv16MGzdOTJ48WenwCi0hIeEfvdsbNGgg5syZI4TIPxdWo48++kj06tVLfPfdd8Lb21t8/PHHeW311ltv5b0+qs3bb78tRowYkXf7o48+Em+88Ybo3Lmzqp9b/4bFQgOV+yTK/eA4depU8fPPPwshhFizZo2qurXq9fq84auPvjjknsy/++67IiMjQyxfvvyxD9GGTq/X53V/z8jIEELk56fW9no0p1w7duwQa9asybv9ySefiL59+6rqTUyv1+e1Ua5hw4aJzMxMsX//flGhQgXRpk0bsWLFCpGSkqJQlIWn1+vFgwcPhBBC3Lx5U/To0UO4uLiIRYsW5T1m4sSJYty4cUqFWGiPtlViYqKoUqWK6Nmzp/jyyy/zHrNmzRrRoUOHvNcVNXi0rXJlZmaK9PR0MWbMGDFt2jQhhPpODvV6vXj48KEQQoioqCjx8ssvi+HDhz9WiFqzZo1o06bNP15bDNWjOZ0+fVrY2tqK/v37i8GDB4tu3boJIYSYPHmy+PDDD4UQ6mszIeTrhb29vfjoo48eu/+TTz4RgwYNUmVOQoi8YpoQ6j/HeJRWzglTU1Pz3mMfPcYuXbokhg0bJsaPHy8iIiLE8uXLxcaNG1VzHKampuYNyc09BnO/fvHFF3nvX6tWrRLbtm1TZV65jh07lleUF0KIRYsWiW7duqnmw39qaqq4f//+Y/cNHz5cREVFiX379glHR0fRuXNn8f7774v4+HiFoiy8R9sqOTlZTJw4UXh5eYnvv/8+7zGzZs0S/fv3V83xJ8Tj7XXv3j3RoUMH8eKLL4oZM2bkPWbv3r2iQ4cOIjU1VakwCyU1NVXcuXPnsftyz2e//PJLMXr0aCGE+s4tUlNT86aeiY2NFf/73//E5MmTRZ8+ffIes3fvXtGmTZu8x6nBo3lFR0cLV1dXERAQIHr37p13Tjhr1iwxcuRIJcMsMeqY1bkUOH/+fN4S7wBgbGwMIH/VO1NTUzg7OyMwMBDvvvsuypQpo0ichbVp0yYMHDgQL7zwAlasWIGEhIS8fQ0bNsSXX36ZN4Ry3LhxKFu2rILRFtyjef3+++9ITEwEgLwJTNXYXrk5de/eHStWrEBsbCwAufpsv3798o5FGxsbuLu7q2LSYCA/Lz8/P6xYsQI3btwAANjZ2WHs2LEYOXIkfvrpJ/Tp0we7du3KW/HU0D3aXr/++isqVKiApUuX4uuvv8bIkSPz2svW1haOjo4KR1swj7bVb7/9Bnt7e2zduhUhISFYu3ZtXk4AYG9v/9htQ5abl7+/P1asWIG4uDgA8vXC3NwcgwcPxk8//YSQkBDVPK+Ax9tr2bJlqFSpEn7//Xf069cPDg4OeY/LXY0wd9VdQ/ZoTr/++iuqV6+O0NBQtGvXDp07d85bvdrIyChvdUU1tNnfzzGcnZ2xbds2fPPNN/joo4/y7rexsYGNjY0qcgKefu6UnZ2t6nMMLZ4T/vHHH+jTpw/atWuH+fPn4+TJk3n7qlWrhmnTpsHExARDhgzBuHHj4OnpqYrjMDev9u3bY/78+Th9+jSA/DYzMTGBk5MTNm3ahA8++EA150+P5vX999/jxIkTAIBGjRqhW7dueceisbExKlWqpGSoBZabU5s2bfD999/j2LFjAICqVatixowZGD16NBYuXIi33noLSUlJqjnHeLSt5s2bh+TkZMyaNQvz58/H66+/nvc4S0tLuLq6quL4Ax5vr2+//Ra3b9/GypUrcfHiRaxduxbR0dEAgDt37sDc3Bx6vV7ZgAsgN6cOHTo89rwyNzcHAHTu3Blr167F1q1bVdNOQH5ebdu2xdy5c3H//n3MmjULr7zyCipUqJD3uKSkJJiZmanms9bf8zIzM0NoaChee+01DB48GH/++WfeY93c3JQLtCQpV6ekXJs2bRI6nU6MHj1aXLp06YmPmTFjhqhWrZpo1KiROH/+/HOOsGj2798vqlatKv766y+xdOlS0apVKzFjxoy8oQy5pkyZIhwcHMS5c+cUirRwnpbXo13J3333XVW119Ny+vvkwKtXr1ZNTkI8Oa93331XREVFiZ07dwovLy+xfv36vMfnLnRi6J6U1/Tp0/MmvM+1Zs0a0bBhQ3Hx4kWFIi24J+X0v//9T0RHR4sLFy4IFxcX8cYbb4gJEyYIX1/ff7yOGKqCvg4OGjRIfPHFF4/1jDJkT3tuXb58Wdy4cUPY2dmJsWPHilmzZomGDRuqYiGJpz2vIiMjH3vcL7/8ImrUqCHCwsIUirRw/u0c48SJE6JixYri5ZdfFsOGDRM1a9ZUzXPrv86dcntlqO0cQ4vnhCEhIaJatWriyJEjYuPGjWLo0KFi7Nix4q+//nrsce+++65wdHRURU5CFCyvzz//XDg6OorGjRurPq9t27Y99riVK1eKRo0aqeK59bScDh06JM6ePSuqVq362Png33tUGqon5TVq1Kh/LHa3atUq1bxeCPHPvIYMGSJGjhwpTp8+LRISEkTLli3FyJEjxbBhw0S9evVU8b5V0OfVpEmTxBtvvPGPEVGG6ml57dy5U6SkpAg3NzcxaNAgMXnyZNWcDwrx9GPw78+tRYsWiVq1aokLFy4oFGnJUvE6ztpw48YNLF26FJ999hlOnDiBBQsWYMyYMXmTbmdnZ8PIyAh37tzBtWvXEBoaavATtua6dOkS/Pz88ibgrlmzJubOnYt169bBxcUFFSpUwJkzZ3Dy5Ens2LEDtWrVUjjignlaXmvXroWjoyMqVqyouvZ6Wk7r169HpUqVYGtri59++glz587Fhg0bULNmTYUjLpgn5fXNN99g6dKlGDRoEHbu3AkPDw/o9XqYmJjk9RQydE/L69dff8Wbb74JOzu7vNeVjRs3onr16gpH/N+eltOiRYvw6aef4vjx4wgODsadO3cwbtw4+Pj4KBxxwRTkdRAAunbtijZt2uT1RjF0//bc+uijj7Br1y588803SEpKwq+//qqK14x/yyn3ebV3716sXr0aq1atQtWqVRWO+L9dv379X88xGjRogDNnzuDAgQNISkrCI5qNDgAADghJREFU9OnTNZEXIHt8hoaGquoc47/yUus54dWrV9GgQQM0bdoUgOxJuGTJEmzcuBFOTk6oW7cuIiMjcf78eWzbtk0VrxfAv+fl6OiIevXqwcjICEIIrFixAt7e3gpHXDBPy2vTpk1wdnZGnTp18Msvv+Dbb7/FihUrVPHcelpOv/32G8aMGYODBw/CwcEBWVlZMDY2hrW1tcIRF8zT8lqzZg3Kly+PunXr4rfffsPs2bOxZs0a1T+3fvjhB8ycORObNm3C5cuXERsbi/fee++xhawM1X89r+rWrQsA8PPzQ40aNWBqaqpkuAX2tLzWrl2LevXqYf/+/fj5559hZmaG5cuXq+IzCVCw59bx48exbds2/P7776hRo4bCEZcQpauVpV1GRkbeZOIRERHCz89PvPXWW/+oTl+9elUVV+4etXz5ctG7d+/H7jt69Kho2rSpWLt2rRBCTkiekJCgRHhF9m955c7tFxMTo5qrd0IUrK2io6P/0cPG0D0pr5CQENG0adPHriCrTUHaKyoq6h89DQ3Zv+W0cuVKhaJ6dgVpKzUqSHupbbEWLb5nPXz4UGzfvl0I8fg5hpren57kaXn9/dxJK+2l1nPC3Hnsdu/eLXr37v1YW1y8eFEMGDAgb47d9PT0f8zjZagKktfChQuFEHJ+0MuXLysSZ2EVJq/ExERx9epVReIsjMIcg2pSmLaKj48XV65cUSLMQvuvvPr37y9++OEHpcIrksK0lZoUpK0enTNTLQrTXmlpaarphVxULBYq5OLFiyImJkYI8fgEpo+eHKakpIhffvlFbN26VakwC2337t0iODhYCCFP0t3d3f+xuMLChQtFnz59VNO9WoiC59W7d2/VLLpQmttqwYIFok+fPqoqZmixvQqTk1qG5wpRuLy0eAz27t1bNZPda/F5JYTM69ChQ0KI/z7HUNPqx4XJ6+9DXA1ZYfJSyznhli1b8lZBv3//vmjVqpV49dVXH3tM7mJVf1/8yZAVJq+/L6RhyLSYV2FyUsu5uxDabCshtNleWsxJiMLlpZbF7YTQ7vvWs+ACJwrYsmULxowZg4yMDAD5k6Pr9Xp4eXnhu+++w/Xr19GtWzdMnjwZLi4uSoZbYEFBQRg/fjxu3bqF7OxsWFpaYvfu3fjjjz8em2BXCAFLS0vVDLcrTF7lypVTRbfx0t5WgJzoWS202F6FzUktEz0XNi+1KOzroJGR4Z9eaPF5BeTnlbugWO5zJysr64nnGBUrVlQy3AIrbF5OTk5Khltghc1LDeeEO3fuxLBhw/Dhhx/ixIkTsLS0RGBgIA4cOIBhw4blLTSWkZEBW1vbvMXhDF1h8zIzM1M44oLRYl6FzUkN71mANtsK0GZ7aTEnoPB5qeXcSavvW89MuTpl6bRt2zZRv359sW/fvn/sy87Ozus988EHH4gKFSqoZqjQ5cuXRZ06dcTRo0eFEHJ4de7VrKtXr4qqVauKnj17isGDB4uaNWv+Y+EMQ6XFvLSYkxDMS015aTEnIZiXmvLSYk5CPDmvJ13V18I5BvMyTNu2bRO+vr5i8+bNYtq0aY8N8bx9+7bo0KGDePHFF0VAQICoWbOmOHXqlHLBFgLzUk9eWsxJCOalpry0mJMQzEtteRUHFgufo8uXLwtXV1fx1VdfCSGEuHXrlpg9e7Z4++23xerVq/Med/bsWdGtWzfVfDgRQogzZ84If39/IYQQkZGRYvDgwaJr165i6tSpIiYmRqSlpYnAwECxfPlyVc2jpsW8tJiTEMxLTXlpMSchmJea8tJiTkI8Pa/p06eLY8eOCSGEOH/+vGbOMZiXYTl+/Ljw8PAQBw8eFEII8dlnnwkvL6/HhtelpaWJY8eOic2bN6tmHjXmpZ68tJiTEMxLTXlpMSchmJfa8iouLBY+Z8OHDxejRo0Sq1evFo0aNRJTpkwRH374oShbtqxYtmyZEEJOrJmUlKRwpIVz8eJF0bNnT5GUlCT69u0rvv/+e7F7924xcOBAMWHCBKXDKzIt5qXFnIRgXmqixZyEYF5qosWchPj3vCZPniyEkL3XtHSOwbwMy98XXnnhhRfyJrlXy1ymT8K81EOLOQnBvNREizkJwbxKIxYLFTBy5Ejh4uIiFixYkHffokWLxODBg1U12f3ftWjRQjRs2FB8+OGHefedOXNGtGjRQiQnJysY2bPRYl5azEkI5qUmWsxJCOalJlrMSYh/z0tNKwP/HfNSj+zsbJGVlSX0er149913xcsvv6x0SMWCeamHFnMSgnmpiRZzEoJ5lTbqmElTxU6cOIFjx44hOzs7775FixZh4cKFGD16dN59d+7cgY2NjWomN31SXn/99RcA4KeffkJKSgoAIDIyUjWLfgDazEuLOQHMS015aTEngHmpKS8t5gQUPi8LCwtF4iws5qWevJ6Uk06ng5GREYyNjTF+/Hjs378fK1asUDDKwmNe6slLizkBzEtNeWkxJ4B5qS2vEqF0tVLLtmzZInQ6nejRo4cICQl5aq/BlStXikaNGokLFy485wiL5t/yunv3rmjSpIno37+/GDNmjGjQoIE4c+aMgtEWnBbz0mJOQjAvNeWlxZyEYF5qykuLOQnBvJiX8v7rPDd30b6JEyeK//3vfyIjI0OJMAuNeaknLy3mJATzUlNeWsxJCOaltrxKik4IIZQuWGpRQkICpk6ditatW+PUqVNITk7GhAkT0Lhx48d6D65btw7Tpk3Dpk2bUKtWLQUjLpiC5rVnzx6kp6ejWrVq8PT0VDDigtFiXlrMCWBeaspLizkBzEtNeWkxJ4B5MS/lFTQnANi9eze8vLzg7u6uULQFx7zUk5cWcwKYF6CevLSYE8C8AHXlVaKUrlZq2aFDh4QQcgWdoUOHipdfflkcPnw4r2IthBCpqani6tWrSoVYJAXJS420mJcWcxKCeamJFnMSgnmpiRZzEoJ5qY0W89JiTkIwLzXRYk5CMC810WJOQjAvktizsJilpKRAr9fDzs7usfvT09MxatQoZGVlYdasWQgKCoKJiclj8xYassLkZWZmhpEjRyoUaeFoMS8t5gQwLzXlpcWcAOalpry0mBPAvJiX8gqTk7m5OUaMGKFQpIXDvNSTlxZzApiXmvLSYk4A81JbXs+F0tVKLfnzzz9F06ZNRfPmzcXYsWNFaGioyM7OztuflpYmxo8fL2rXri0cHR3FyZMnFYy24JiXevLSYk5CMC815aXFnIRgXmrKS4s5CcG8mJfytJiTEMxLTXlpMSchmJea8tJiTkIwL7Xl9bywWFhMjh07JqpVqyYOHjwoIiMjRZ8+fcTw4cPFtm3bHjsg58+fL+zt7cX58+cVjLbgmJd68tJiTkIwLzXlpcWchGBeaspLizkJwbyYl/K0mJMQzEtNeWkxJyGYl5ry0mJOQjAvteX1PBn9d99DKoj79++jadOmaNmyJTw9PbFkyRJYW1tjzZo1uHz5MgAgIiIChw4dwq5du1CzZk2FIy4Y5qWevLSYE8C81JSXFnMCmJea8tJiTgDzYl7K02JOAPNSU15azAlgXmrKS4s5AcxLbXk9V0pXK7Xi1KlTwtfXV/y/vftnpf4B4zj+PjERJolFoiwsRnaDslh4ChaKR2ARj8RqYSKjEjGxWZSS8i8MDNe9yPbrPvHr/nZdvV/jOcv1rjN9+p5zHh8fv197eXmJ+fn5WFlZ+X7t+fm5get+zq48XRWbIuzK1FWxKcKuTF0VmyLssqt5FZsi7MrUVbEpwq5MXRWbIuzK1vUv+WThLxwdHbG5ucnGxgbj4+PMzMwwMzPz/X5PTw/b29tcXV3x+voKQG9vb1Pnts2uPF0Vm8CuTF0Vm8CuTF0Vm8Auu5pXsQnsytRVsQnsytRVsQnsytbVFMfCH9rb22NtbY2I4Orqirm5Oba2thgdHWVycpK7uzsATk9P+fj4oLOzs+GL22NXnq6KTWBXpq6KTWBXpq6KTWCXXc2r2AR2Zeqq2AR2Zeqq2AR2Zetq1L98jLGKs7OzGB8fj7Ozs4iIuL29jdnZ2bi+vo6IiKWlpZieno6FhYWYmJiIi4uLJs9tm115uio2RdiVqatiU4RdmboqNkXYZVfzKjZF2JWpq2JThF2Zuio2RdiVratpzqk/0NfXx/LyMlNTUwAMDg7y/v7OyckJIyMj7OzscHl5yfPzM0NDQwwPDzd8cXvsytNVsQnsytRVsQnsytRVsQnssqt5FZvArkxdFZvArkxdFZvArmxdjWt6rczq7e0tIiI+Pz8jImJxcTEODw8jImJ/fz/u7+8bu+037MqjYlOEXZlUbIqwK5OKTRF2ZVOxq2JThF2ZVGyKsCuTik0Rdql9/mbhD3V1dQHQarUA6OzspL+/n93dXVZXV3l6emrwup+zK4+KTWBXJhWbwK5MKjaBXdlU7KrYBHZlUrEJ7MqkYhPYpfb5NeRf6ujoAKC7u5v19XUeHh7Y3d1lbGys4ct+x648KjaBXZlUbAK7MqnYBHZlU7GrYhPYlUnFJrArk4pNYJf+zrHwlyKCVqvFzc0N5+fnHB8fl/gg2pVHxSawK5OKTWBXJhWbwK5sKnZVbAK7MqnYBHZlUrEJ7NLftSIimj6igoODAwYGBpicnGz6lP+VXXlUbAK7MqnYBHZlUrEJ7MqmYlfFJrArk4pNYFcmFZvALv03x0JJkiRJkiRJAPgHJ5IkSZIkSZIAx0JJkiRJkiRJXxwLJUmSJEmSJAGOhZIkSZIkSZK+OBZKkiRJkiRJAhwLJUmSJEmSJH1xLJQkSZIkSZIEOBZKkiRJkiRJ+vIHUv2FRCyKGLoAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "\n",
    "# 分组,一图多线\n",
    "#resample降采样，按月统计,索引必须是时间类型，类似groupby\n",
    "for group_name, group_data in df.groupby(by=\"cate\"):\n",
    "    # 对不同的分类都进行绘图，resample降采样，按月统计,索引必须是时间类型，类似groupby\n",
    "    count_by_month = group_data.resample(\"MS\").count()[\"title\"]  #降采样\n",
    "    print(count_by_month)\n",
    "    # 画图\n",
    "    _x = count_by_month.index\n",
    "    # print(_x)\n",
    "    _y = count_by_month.values #values是对应事故发生次数\n",
    "\n",
    "    _x = [i.strftime(\"%Y%m%d\") for i in _x]  #变年月日格式\n",
    "\n",
    "    plt.plot(_x, _y, label=group_name)\n",
    "\n",
    "plt.xticks(range(len(_x)), _x, rotation=45)\n",
    "plt.grid(alpha=0.5)\n",
    "plt.legend(loc=\"best\")\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T03:13:08.334064900Z",
     "start_time": "2024-07-08T03:13:07.134841700Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# pm2.5 实战"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No                     int64\n",
      "year                   int64\n",
      "month                  int64\n",
      "day                    int64\n",
      "hour                   int64\n",
      "season                 int64\n",
      "PM_Dongsi            float64\n",
      "PM_Dongsihuan        float64\n",
      "PM_Nongzhanguan      float64\n",
      "PM_US Post           float64\n",
      "DEWP                 float64\n",
      "HUMI                 float64\n",
      "PRES                 float64\n",
      "TEMP                 float64\n",
      "cbwd                  object\n",
      "Iws                  float64\n",
      "precipitation        float64\n",
      "Iprec                float64\n",
      "datetime           period[h]\n",
      "dtype: object\n"
     ]
    },
    {
     "data": {
      "text/plain": "    No  year  month  day  hour  season  PM_Dongsi  PM_Dongsihuan  \\\n0    1  2010      1    1     0       4        NaN            NaN   \n1    2  2010      1    1     1       4        NaN            NaN   \n2    3  2010      1    1     2       4        NaN            NaN   \n3    4  2010      1    1     3       4        NaN            NaN   \n4    5  2010      1    1     4       4        NaN            NaN   \n5    6  2010      1    1     5       4        NaN            NaN   \n6    7  2010      1    1     6       4        NaN            NaN   \n7    8  2010      1    1     7       4        NaN            NaN   \n8    9  2010      1    1     8       4        NaN            NaN   \n9   10  2010      1    1     9       4        NaN            NaN   \n10  11  2010      1    1    10       4        NaN            NaN   \n11  12  2010      1    1    11       4        NaN            NaN   \n12  13  2010      1    1    12       4        NaN            NaN   \n13  14  2010      1    1    13       4        NaN            NaN   \n14  15  2010      1    1    14       4        NaN            NaN   \n15  16  2010      1    1    15       4        NaN            NaN   \n16  17  2010      1    1    16       4        NaN            NaN   \n17  18  2010      1    1    17       4        NaN            NaN   \n18  19  2010      1    1    18       4        NaN            NaN   \n19  20  2010      1    1    19       4        NaN            NaN   \n20  21  2010      1    1    20       4        NaN            NaN   \n21  22  2010      1    1    21       4        NaN            NaN   \n22  23  2010      1    1    22       4        NaN            NaN   \n23  24  2010      1    1    23       4        NaN            NaN   \n24  25  2010      1    2     0       4        NaN            NaN   \n25  26  2010      1    2     1       4        NaN            NaN   \n26  27  2010      1    2     2       4        NaN            NaN   \n27  28  2010      1    2     3       4        NaN            NaN   \n28  29  2010      1    2     4       4        NaN            NaN   \n29  30  2010      1    2     5       4        NaN            NaN   \n\n    PM_Nongzhanguan  PM_US Post  DEWP  HUMI    PRES  TEMP cbwd    Iws  \\\n0               NaN         NaN -21.0  43.0  1021.0 -11.0   NW   1.79   \n1               NaN         NaN -21.0  47.0  1020.0 -12.0   NW   4.92   \n2               NaN         NaN -21.0  43.0  1019.0 -11.0   NW   6.71   \n3               NaN         NaN -21.0  55.0  1019.0 -14.0   NW   9.84   \n4               NaN         NaN -20.0  51.0  1018.0 -12.0   NW  12.97   \n5               NaN         NaN -19.0  47.0  1017.0 -10.0   NW  16.10   \n6               NaN         NaN -19.0  44.0  1017.0  -9.0   NW  19.23   \n7               NaN         NaN -19.0  44.0  1017.0  -9.0   NW  21.02   \n8               NaN         NaN -19.0  44.0  1017.0  -9.0   NW  24.15   \n9               NaN         NaN -20.0  37.0  1017.0  -8.0   NW  27.28   \n10              NaN         NaN -19.0  37.0  1017.0  -7.0   NW  31.30   \n11              NaN         NaN -18.0  35.0  1017.0  -5.0   NW  34.43   \n12              NaN         NaN -19.0  32.0  1015.0  -5.0   NW  37.56   \n13              NaN         NaN -18.0  30.0  1015.0  -3.0   NW  40.69   \n14              NaN         NaN -18.0  28.0  1014.0  -2.0   NW  43.82   \n15              NaN         NaN -18.0  26.0  1014.0  -1.0   cv   0.89   \n16              NaN         NaN -19.0  25.0  1015.0  -2.0   NW   1.79   \n17              NaN         NaN -18.0  30.0  1015.0  -3.0   NW   2.68   \n18              NaN         NaN -18.0  35.0  1016.0  -5.0   NE   1.79   \n19              NaN         NaN -17.0  35.0  1017.0  -4.0   NW   1.79   \n20              NaN         NaN -17.0  38.0  1017.0  -5.0   cv   0.89   \n21              NaN         NaN -17.0  38.0  1018.0  -5.0   NW   1.79   \n22              NaN         NaN -17.0  38.0  1018.0  -5.0   NW   2.68   \n23              NaN       129.0 -17.0  41.0  1020.0  -5.0   cv   0.89   \n24              NaN       148.0 -16.0  38.0  1020.0  -4.0   SE   1.79   \n25              NaN       159.0 -15.0  42.0  1020.0  -4.0   SE   2.68   \n26              NaN       181.0 -11.0  63.5  1021.0  -5.0   SE   3.57   \n27              NaN       138.0  -7.0  85.0  1022.0  -5.0   SE   5.36   \n28              NaN       109.0  -7.0  85.0  1022.0  -5.0   SE   6.25   \n29              NaN       105.0  -7.0  92.0  1022.0  -6.0   SE   7.14   \n\n    precipitation  Iprec          datetime  \n0             0.0    0.0  2010-01-01 00:00  \n1             0.0    0.0  2010-01-01 01:00  \n2             0.0    0.0  2010-01-01 02:00  \n3             0.0    0.0  2010-01-01 03:00  \n4             0.0    0.0  2010-01-01 04:00  \n5             0.0    0.0  2010-01-01 05:00  \n6             0.0    0.0  2010-01-01 06:00  \n7             0.0    0.0  2010-01-01 07:00  \n8             0.0    0.0  2010-01-01 08:00  \n9             0.0    0.0  2010-01-01 09:00  \n10            0.0    0.0  2010-01-01 10:00  \n11            0.0    0.0  2010-01-01 11:00  \n12            0.0    0.0  2010-01-01 12:00  \n13            0.0    0.0  2010-01-01 13:00  \n14            0.0    0.0  2010-01-01 14:00  \n15            0.0    0.0  2010-01-01 15:00  \n16            0.0    0.0  2010-01-01 16:00  \n17            0.0    0.0  2010-01-01 17:00  \n18            0.0    0.0  2010-01-01 18:00  \n19            0.0    0.0  2010-01-01 19:00  \n20            0.0    0.0  2010-01-01 20:00  \n21            0.0    0.0  2010-01-01 21:00  \n22            0.0    0.0  2010-01-01 22:00  \n23            0.0    0.0  2010-01-01 23:00  \n24            0.0    0.0  2010-01-02 00:00  \n25            0.0    0.0  2010-01-02 01:00  \n26            0.0    0.0  2010-01-02 02:00  \n27            0.0    0.0  2010-01-02 03:00  \n28            0.0    0.0  2010-01-02 04:00  \n29            0.0    0.0  2010-01-02 05:00  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>No</th>\n      <th>year</th>\n      <th>month</th>\n      <th>day</th>\n      <th>hour</th>\n      <th>season</th>\n      <th>PM_Dongsi</th>\n      <th>PM_Dongsihuan</th>\n      <th>PM_Nongzhanguan</th>\n      <th>PM_US Post</th>\n      <th>DEWP</th>\n      <th>HUMI</th>\n      <th>PRES</th>\n      <th>TEMP</th>\n      <th>cbwd</th>\n      <th>Iws</th>\n      <th>precipitation</th>\n      <th>Iprec</th>\n      <th>datetime</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-21.0</td>\n      <td>43.0</td>\n      <td>1021.0</td>\n      <td>-11.0</td>\n      <td>NW</td>\n      <td>1.79</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 00:00</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-21.0</td>\n      <td>47.0</td>\n      <td>1020.0</td>\n      <td>-12.0</td>\n      <td>NW</td>\n      <td>4.92</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 01:00</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-21.0</td>\n      <td>43.0</td>\n      <td>1019.0</td>\n      <td>-11.0</td>\n      <td>NW</td>\n      <td>6.71</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 02:00</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-21.0</td>\n      <td>55.0</td>\n      <td>1019.0</td>\n      <td>-14.0</td>\n      <td>NW</td>\n      <td>9.84</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 03:00</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-20.0</td>\n      <td>51.0</td>\n      <td>1018.0</td>\n      <td>-12.0</td>\n      <td>NW</td>\n      <td>12.97</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 04:00</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>6</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>5</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-19.0</td>\n      <td>47.0</td>\n      <td>1017.0</td>\n      <td>-10.0</td>\n      <td>NW</td>\n      <td>16.10</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 05:00</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>7</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>6</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-19.0</td>\n      <td>44.0</td>\n      <td>1017.0</td>\n      <td>-9.0</td>\n      <td>NW</td>\n      <td>19.23</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 06:00</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>8</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>7</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-19.0</td>\n      <td>44.0</td>\n      <td>1017.0</td>\n      <td>-9.0</td>\n      <td>NW</td>\n      <td>21.02</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 07:00</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>9</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>8</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-19.0</td>\n      <td>44.0</td>\n      <td>1017.0</td>\n      <td>-9.0</td>\n      <td>NW</td>\n      <td>24.15</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 08:00</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>10</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>9</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-20.0</td>\n      <td>37.0</td>\n      <td>1017.0</td>\n      <td>-8.0</td>\n      <td>NW</td>\n      <td>27.28</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 09:00</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>11</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>10</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-19.0</td>\n      <td>37.0</td>\n      <td>1017.0</td>\n      <td>-7.0</td>\n      <td>NW</td>\n      <td>31.30</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 10:00</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>12</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>11</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-18.0</td>\n      <td>35.0</td>\n      <td>1017.0</td>\n      <td>-5.0</td>\n      <td>NW</td>\n      <td>34.43</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 11:00</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>13</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>12</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-19.0</td>\n      <td>32.0</td>\n      <td>1015.0</td>\n      <td>-5.0</td>\n      <td>NW</td>\n      <td>37.56</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 12:00</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>14</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>13</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-18.0</td>\n      <td>30.0</td>\n      <td>1015.0</td>\n      <td>-3.0</td>\n      <td>NW</td>\n      <td>40.69</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 13:00</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>15</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>14</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-18.0</td>\n      <td>28.0</td>\n      <td>1014.0</td>\n      <td>-2.0</td>\n      <td>NW</td>\n      <td>43.82</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 14:00</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>16</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>15</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-18.0</td>\n      <td>26.0</td>\n      <td>1014.0</td>\n      <td>-1.0</td>\n      <td>cv</td>\n      <td>0.89</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 15:00</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>17</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>16</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-19.0</td>\n      <td>25.0</td>\n      <td>1015.0</td>\n      <td>-2.0</td>\n      <td>NW</td>\n      <td>1.79</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 16:00</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>18</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>17</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-18.0</td>\n      <td>30.0</td>\n      <td>1015.0</td>\n      <td>-3.0</td>\n      <td>NW</td>\n      <td>2.68</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 17:00</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>19</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>18</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-18.0</td>\n      <td>35.0</td>\n      <td>1016.0</td>\n      <td>-5.0</td>\n      <td>NE</td>\n      <td>1.79</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 18:00</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>20</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>19</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-17.0</td>\n      <td>35.0</td>\n      <td>1017.0</td>\n      <td>-4.0</td>\n      <td>NW</td>\n      <td>1.79</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 19:00</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>21</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>20</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-17.0</td>\n      <td>38.0</td>\n      <td>1017.0</td>\n      <td>-5.0</td>\n      <td>cv</td>\n      <td>0.89</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 20:00</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>22</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>21</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-17.0</td>\n      <td>38.0</td>\n      <td>1018.0</td>\n      <td>-5.0</td>\n      <td>NW</td>\n      <td>1.79</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 21:00</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>23</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>22</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>-17.0</td>\n      <td>38.0</td>\n      <td>1018.0</td>\n      <td>-5.0</td>\n      <td>NW</td>\n      <td>2.68</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 22:00</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>24</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>23</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>129.0</td>\n      <td>-17.0</td>\n      <td>41.0</td>\n      <td>1020.0</td>\n      <td>-5.0</td>\n      <td>cv</td>\n      <td>0.89</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 23:00</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>25</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>2</td>\n      <td>0</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>148.0</td>\n      <td>-16.0</td>\n      <td>38.0</td>\n      <td>1020.0</td>\n      <td>-4.0</td>\n      <td>SE</td>\n      <td>1.79</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-02 00:00</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>26</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>2</td>\n      <td>1</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>159.0</td>\n      <td>-15.0</td>\n      <td>42.0</td>\n      <td>1020.0</td>\n      <td>-4.0</td>\n      <td>SE</td>\n      <td>2.68</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-02 01:00</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>27</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>2</td>\n      <td>2</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>181.0</td>\n      <td>-11.0</td>\n      <td>63.5</td>\n      <td>1021.0</td>\n      <td>-5.0</td>\n      <td>SE</td>\n      <td>3.57</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-02 02:00</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>28</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>2</td>\n      <td>3</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>138.0</td>\n      <td>-7.0</td>\n      <td>85.0</td>\n      <td>1022.0</td>\n      <td>-5.0</td>\n      <td>SE</td>\n      <td>5.36</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-02 03:00</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>29</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>109.0</td>\n      <td>-7.0</td>\n      <td>85.0</td>\n      <td>1022.0</td>\n      <td>-5.0</td>\n      <td>SE</td>\n      <td>6.25</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-02 04:00</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>30</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>2</td>\n      <td>5</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>105.0</td>\n      <td>-7.0</td>\n      <td>92.0</td>\n      <td>1022.0</td>\n      <td>-6.0</td>\n      <td>SE</td>\n      <td>7.14</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-02 05:00</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "file_path = \"./PM2.5/BeijingPM20100101_20151231.csv\"\n",
    "\n",
    "df = pd.read_csv(file_path)\n",
    "# print(df.head(10))\n",
    "\n",
    "# 把分开的时间字符串通过periodIndex的方法转化为pandas的时间类型\n",
    "period = pd.PeriodIndex.from_fields(year=df[\"year\"], month=df[\"month\"], day=df[\"day\"],\n",
    "                        hour=df[\"hour\"], freq=\"h\")\n",
    "df[\"datetime\"] = period\n",
    "print(df.dtypes)\n",
    "df.head(30)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T03:30:08.308512Z",
     "start_time": "2024-07-08T03:30:07.969701600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 52584 entries, 0 to 52583\n",
      "Data columns (total 19 columns):\n",
      " #   Column           Non-Null Count  Dtype    \n",
      "---  ------           --------------  -----    \n",
      " 0   No               52584 non-null  int64    \n",
      " 1   year             52584 non-null  int64    \n",
      " 2   month            52584 non-null  int64    \n",
      " 3   day              52584 non-null  int64    \n",
      " 4   hour             52584 non-null  int64    \n",
      " 5   season           52584 non-null  int64    \n",
      " 6   PM_Dongsi        25052 non-null  float64  \n",
      " 7   PM_Dongsihuan    20508 non-null  float64  \n",
      " 8   PM_Nongzhanguan  24931 non-null  float64  \n",
      " 9   PM_US Post       50387 non-null  float64  \n",
      " 10  DEWP             52579 non-null  float64  \n",
      " 11  HUMI             52245 non-null  float64  \n",
      " 12  PRES             52245 non-null  float64  \n",
      " 13  TEMP             52579 non-null  float64  \n",
      " 14  cbwd             52579 non-null  object   \n",
      " 15  Iws              52579 non-null  float64  \n",
      " 16  precipitation    52100 non-null  float64  \n",
      " 17  Iprec            52100 non-null  float64  \n",
      " 18  datetime         52584 non-null  period[h]\n",
      "dtypes: float64(11), int64(6), object(1), period[h](1)\n",
      "memory usage: 7.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-08T03:25:24.735509200Z",
     "start_time": "2024-07-08T03:25:24.708897500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "data": {
      "text/plain": "   No  year  month  day  hour  season  PM_Dongsi  PM_Dongsihuan  \\\n0   1  2010      1    1     0       4        NaN            NaN   \n1   2  2010      1    1     1       4        NaN            NaN   \n2   3  2010      1    1     2       4        NaN            NaN   \n3   4  2010      1    1     3       4        NaN            NaN   \n4   5  2010      1    1     4       4        NaN            NaN   \n\n   PM_Nongzhanguan  PM_US Post  ...  HUMI    PRES  TEMP  cbwd    Iws  \\\n0              NaN         NaN  ...  43.0  1021.0 -11.0    NW   1.79   \n1              NaN         NaN  ...  47.0  1020.0 -12.0    NW   4.92   \n2              NaN         NaN  ...  43.0  1019.0 -11.0    NW   6.71   \n3              NaN         NaN  ...  55.0  1019.0 -14.0    NW   9.84   \n4              NaN         NaN  ...  51.0  1018.0 -12.0    NW  12.97   \n\n   precipitation  Iprec          datetime  date_str       date  \n0            0.0    0.0  2010-01-01 00:00  2010-1-1 2010-01-01  \n1            0.0    0.0  2010-01-01 01:00  2010-1-1 2010-01-01  \n2            0.0    0.0  2010-01-01 02:00  2010-1-1 2010-01-01  \n3            0.0    0.0  2010-01-01 03:00  2010-1-1 2010-01-01  \n4            0.0    0.0  2010-01-01 04:00  2010-1-1 2010-01-01  \n\n[5 rows x 21 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>No</th>\n      <th>year</th>\n      <th>month</th>\n      <th>day</th>\n      <th>hour</th>\n      <th>season</th>\n      <th>PM_Dongsi</th>\n      <th>PM_Dongsihuan</th>\n      <th>PM_Nongzhanguan</th>\n      <th>PM_US Post</th>\n      <th>...</th>\n      <th>HUMI</th>\n      <th>PRES</th>\n      <th>TEMP</th>\n      <th>cbwd</th>\n      <th>Iws</th>\n      <th>precipitation</th>\n      <th>Iprec</th>\n      <th>datetime</th>\n      <th>date_str</th>\n      <th>date</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>43.0</td>\n      <td>1021.0</td>\n      <td>-11.0</td>\n      <td>NW</td>\n      <td>1.79</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 00:00</td>\n      <td>2010-1-1</td>\n      <td>2010-01-01</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>47.0</td>\n      <td>1020.0</td>\n      <td>-12.0</td>\n      <td>NW</td>\n      <td>4.92</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 01:00</td>\n      <td>2010-1-1</td>\n      <td>2010-01-01</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>2</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>43.0</td>\n      <td>1019.0</td>\n      <td>-11.0</td>\n      <td>NW</td>\n      <td>6.71</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 02:00</td>\n      <td>2010-1-1</td>\n      <td>2010-01-01</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>3</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>55.0</td>\n      <td>1019.0</td>\n      <td>-14.0</td>\n      <td>NW</td>\n      <td>9.84</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 03:00</td>\n      <td>2010-1-1</td>\n      <td>2010-01-01</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>2010</td>\n      <td>1</td>\n      <td>1</td>\n      <td>4</td>\n      <td>4</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>...</td>\n      <td>51.0</td>\n      <td>1018.0</td>\n      <td>-12.0</td>\n      <td>NW</td>\n      <td>12.97</td>\n      <td>0.0</td>\n      <td>0.0</td>\n      <td>2010-01-01 04:00</td>\n      <td>2010-1-1</td>\n      <td>2010-01-01</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df['date_str'] = df['year'].astype(str) + '-' + df['month'].astype(str) + '-' + df['day'].astype(str)\n",
    "\n",
    "# 将拼接的字符串列转换为日期时间格式\n",
    "df['date'] = pd.to_datetime(df['date_str'])\n",
    "\n",
    "df.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-08T03:32:03.032111800Z",
     "start_time": "2024-07-08T03:32:02.844640300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "outputs": [],
   "source": [
    "\n",
    "# 把datetime 设置为行索引\n",
    "df.set_index(\"date\", inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T03:32:17.248621600Z",
     "start_time": "2024-07-08T03:32:17.236633100Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "outputs": [
    {
     "data": {
      "text/plain": "               No    year     month        day  hour  season  PM_Dongsi  \\\ndate                                                                      \n2010-01-01   84.5  2010.0  1.000000   4.000000  11.5     4.0        NaN   \n2010-01-08  252.5  2010.0  1.000000  11.000000  11.5     4.0        NaN   \n2010-01-15  420.5  2010.0  1.000000  18.000000  11.5     4.0        NaN   \n2010-01-22  588.5  2010.0  1.000000  25.000000  11.5     4.0        NaN   \n2010-01-29  756.5  2010.0  1.571429  14.285714  11.5     4.0        NaN   \n\n            PM_Dongsihuan  PM_Nongzhanguan  PM_US Post       DEWP       HUMI  \\\ndate                                                                           \n2010-01-01            NaN              NaN   71.627586 -18.255952  54.395833   \n2010-01-08            NaN              NaN   69.910714 -19.035714  49.386905   \n2010-01-15            NaN              NaN  163.654762 -12.630952  57.755952   \n2010-01-22            NaN              NaN   68.069307 -17.404762  34.095238   \n2010-01-29            NaN              NaN   53.583333 -17.565476  34.928571   \n\n                   PRES       TEMP        Iws  precipitation     Iprec  \ndate                                                                    \n2010-01-01  1027.910714 -10.202381  43.859821       0.066667  0.786905  \n2010-01-08  1030.035714 -10.029762  45.392083       0.000000  0.000000  \n2010-01-15  1030.386905  -4.946429  17.492976       0.000000  0.000000  \n2010-01-22  1026.196429  -2.672619  54.854048       0.000000  0.000000  \n2010-01-29  1025.273810  -2.083333  26.625119       0.000000  0.000000  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>No</th>\n      <th>year</th>\n      <th>month</th>\n      <th>day</th>\n      <th>hour</th>\n      <th>season</th>\n      <th>PM_Dongsi</th>\n      <th>PM_Dongsihuan</th>\n      <th>PM_Nongzhanguan</th>\n      <th>PM_US Post</th>\n      <th>DEWP</th>\n      <th>HUMI</th>\n      <th>PRES</th>\n      <th>TEMP</th>\n      <th>Iws</th>\n      <th>precipitation</th>\n      <th>Iprec</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2010-01-01</th>\n      <td>84.5</td>\n      <td>2010.0</td>\n      <td>1.000000</td>\n      <td>4.000000</td>\n      <td>11.5</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>71.627586</td>\n      <td>-18.255952</td>\n      <td>54.395833</td>\n      <td>1027.910714</td>\n      <td>-10.202381</td>\n      <td>43.859821</td>\n      <td>0.066667</td>\n      <td>0.786905</td>\n    </tr>\n    <tr>\n      <th>2010-01-08</th>\n      <td>252.5</td>\n      <td>2010.0</td>\n      <td>1.000000</td>\n      <td>11.000000</td>\n      <td>11.5</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>69.910714</td>\n      <td>-19.035714</td>\n      <td>49.386905</td>\n      <td>1030.035714</td>\n      <td>-10.029762</td>\n      <td>45.392083</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2010-01-15</th>\n      <td>420.5</td>\n      <td>2010.0</td>\n      <td>1.000000</td>\n      <td>18.000000</td>\n      <td>11.5</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>163.654762</td>\n      <td>-12.630952</td>\n      <td>57.755952</td>\n      <td>1030.386905</td>\n      <td>-4.946429</td>\n      <td>17.492976</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2010-01-22</th>\n      <td>588.5</td>\n      <td>2010.0</td>\n      <td>1.000000</td>\n      <td>25.000000</td>\n      <td>11.5</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>68.069307</td>\n      <td>-17.404762</td>\n      <td>34.095238</td>\n      <td>1026.196429</td>\n      <td>-2.672619</td>\n      <td>54.854048</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2010-01-29</th>\n      <td>756.5</td>\n      <td>2010.0</td>\n      <td>1.571429</td>\n      <td>14.285714</td>\n      <td>11.5</td>\n      <td>4.0</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>53.583333</td>\n      <td>-17.565476</td>\n      <td>34.928571</td>\n      <td>1025.273810</td>\n      <td>-2.083333</td>\n      <td>26.625119</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 进行降采样，进行降采样，行索引必须是pd的时间类型\n",
    "df = df.resample(\"7D\").mean(numeric_only=True)\n",
    "df.head()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T03:32:45.252019800Z",
     "start_time": "2024-07-08T03:32:45.195320500Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "outputs": [
    {
     "data": {
      "text/plain": "No                 52500.500000\nyear                2015.000000\nmonth                 12.000000\nday                   28.000000\nhour                  11.500000\nseason                 4.000000\nPM_Dongsi            206.271084\nPM_Dongsihuan        219.377358\nPM_Nongzhanguan      199.566265\nPM_US Post           209.244048\nDEWP                  -8.559524\nHUMI                  70.136905\nPRES                1029.934524\nTEMP                  -3.202381\nIws                    8.713750\nprecipitation          0.000000\nIprec                  0.000000\nName: 2015-12-25 00:00:00, dtype: float64"
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[312] #因为前面行没有pm2.5数据，所以取中间一行"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T03:33:10.187301700Z",
     "start_time": "2024-07-08T03:33:10.126114400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "DatetimeIndex: 155 entries, 2013-01-11 to 2015-12-25\n",
      "Freq: 7D\n",
      "Series name: PM_US Post\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "155 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 2.4 KB\n",
      "--------------------------------------------------\n",
      "date\n",
      "2013-01-11     65.333333\n",
      "2013-01-18    161.054217\n",
      "2013-01-25    224.006098\n",
      "2013-02-01     61.145570\n",
      "2013-02-08    110.478788\n",
      "                 ...    \n",
      "2014-11-07     51.617284\n",
      "2014-11-14    149.935065\n",
      "2014-11-21    142.590062\n",
      "2014-11-28     82.012346\n",
      "2014-12-05    105.767857\n",
      "Freq: 7D, Name: PM_Nongzhanguan, Length: 100, dtype: float64\n",
      "<class 'pandas.core.series.Series'>\n",
      "DatetimeIndex: 155 entries, 2013-01-11 to 2015-12-25\n",
      "Freq: 7D\n",
      "Series name: PM_Nongzhanguan\n",
      "Non-Null Count  Dtype  \n",
      "--------------  -----  \n",
      "155 non-null    float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 2.4 KB\n"
     ]
    }
   ],
   "source": [
    "#对比农展馆和美国测试的数据\n",
    "df= df.loc[:,[\"PM_US Post\",\"PM_Nongzhanguan\"]]\n",
    "df=df.dropna()  #任何一列为空，整行都删掉（整行，就是整个样本）\n",
    "data=df.loc[:,\"PM_US Post\"] #美国大使馆数据\n",
    "data_china = df.loc[:,\"PM_Nongzhanguan\"]  #农展馆数据\n",
    "\n",
    "\n",
    "data.info()\n",
    "print('-'*50)\n",
    "print(data_china.head(100))\n",
    "data_china.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T03:34:00.676069Z",
     "start_time": "2024-07-08T03:34:00.632822Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DatetimeIndex(['2013-01-11', '2013-01-18', '2013-01-25', '2013-02-01',\n",
      "               '2013-02-08', '2013-02-15', '2013-02-22', '2013-03-01',\n",
      "               '2013-03-08', '2013-03-15',\n",
      "               ...\n",
      "               '2015-10-23', '2015-10-30', '2015-11-06', '2015-11-13',\n",
      "               '2015-11-20', '2015-11-27', '2015-12-04', '2015-12-11',\n",
      "               '2015-12-18', '2015-12-25'],\n",
      "              dtype='datetime64[ns]', name='date', length=155, freq='7D')\n",
      "--------------------------------------------------\n",
      "['20130111', '20130118', '20130125', '20130201', '20130208', '20130215', '20130222', '20130301', '20130308', '20130315', '20130322', '20130329', '20130405', '20130412', '20130419', '20130426', '20130503', '20130510', '20130517', '20130524', '20130531', '20130607', '20130614', '20130621', '20130628', '20130705', '20130712', '20130719', '20130726', '20130802', '20130809', '20130816', '20130823', '20130830', '20130906', '20130913', '20130920', '20130927', '20131004', '20131011', '20131018', '20131025', '20131101', '20131108', '20131115', '20131122', '20131129', '20131206', '20131213', '20131220', '20131227', '20140103', '20140110', '20140117', '20140124', '20140131', '20140207', '20140214', '20140221', '20140228', '20140307', '20140314', '20140321', '20140328', '20140404', '20140411', '20140418', '20140425', '20140502', '20140509', '20140516', '20140523', '20140530', '20140606', '20140613', '20140620', '20140627', '20140704', '20140711', '20140718', '20140725', '20140801', '20140808', '20140815', '20140822', '20140829', '20140905', '20140912', '20140919', '20140926', '20141003', '20141010', '20141017', '20141024', '20141031', '20141107', '20141114', '20141121', '20141128', '20141205', '20141212', '20141219', '20141226', '20150102', '20150109', '20150116', '20150123', '20150130', '20150206', '20150213', '20150220', '20150227', '20150306', '20150313', '20150320', '20150327', '20150403', '20150410', '20150417', '20150424', '20150501', '20150508', '20150515', '20150522', '20150529', '20150605', '20150612', '20150619', '20150626', '20150703', '20150710', '20150717', '20150724', '20150731', '20150807', '20150814', '20150821', '20150828', '20150904', '20150911', '20150918', '20150925', '20151002', '20151009', '20151016', '20151023', '20151030', '20151106', '20151113', '20151120', '20151127', '20151204', '20151211', '20151218', '20151225']\n",
      "155 155\n"
     ]
    }
   ],
   "source": [
    "# 画图\n",
    "_x = data.index\n",
    "print(_x)\n",
    "print('-'*50)\n",
    "_x = [i.strftime(\"%Y%m%d\") for i in _x]\n",
    "print(_x)\n",
    "_x_china = [i.strftime(\"%Y%m%d\") for i in data_china.index]\n",
    "print(len(_x), len(_x_china))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T03:34:21.968151100Z",
     "start_time": "2024-07-08T03:34:21.927075400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 1600x640 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "_y = data.values  #美国的\n",
    "_y_china = data_china.values  #农展馆\n",
    "\n",
    "plt.figure(figsize=(20, 8), dpi=80)\n",
    "\n",
    "plt.plot(range(len(_x)), _y, label=\"US_POST\", alpha=0.7)\n",
    "plt.plot(range(len(_x_china)), _y_china, label=\"CN_POST\", alpha=0.7)\n",
    "\n",
    "#第一个参数是刻度值，\n",
    "plt.xticks(range(0, len(_x_china), 5), _x_china[::5], rotation=45)\n",
    "plt.grid(alpha=0.5)\n",
    "plt.legend(loc=\"best\")\n",
    "\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T03:35:28.256415100Z",
     "start_time": "2024-07-08T03:35:27.538293900Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "### 分组运算"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  key1  data1  data2\n",
      "0    a      6      4\n",
      "1    b      2      9\n",
      "2    a      1      2\n",
      "3    b      2      2\n",
      "4    a      4      4\n",
      "5    b      3      8\n",
      "6    a      2      3\n",
      "7    a      2      6\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "#分组后给名称加前缀\n",
    "dict_obj = {'key1' : ['a', 'b', 'a', 'b',\n",
    "                      'a', 'b', 'a', 'a'],\n",
    "            'data1': np.random.randint(1, 10, 8),\n",
    "            'data2': np.random.randint(1, 10, 8)}\n",
    "df_obj = pd.DataFrame(dict_obj)\n",
    "print(df_obj)\n",
    "print('-'*50)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T03:39:52.573272800Z",
     "start_time": "2024-07-08T03:39:52.560639400Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      mean_data1  mean_data2\n",
      "key1                        \n",
      "a       3.000000    3.800000\n",
      "b       2.333333    6.333333\n"
     ]
    }
   ],
   "source": [
    "# 按key1分组后，计算data1，data2的统计信息并附加到原始表格中，并添加表头前缀\n",
    "k1_sum = df_obj.groupby('key1').mean(numeric_only=True).add_prefix('mean_')\n",
    "print(k1_sum)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-07-08T03:39:54.902782200Z",
     "start_time": "2024-07-08T03:39:54.832325200Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [
    {
     "data": {
      "text/plain": "   mean_data1  mean_data2\n0    3.000000    3.800000\n1    2.333333    6.333333\n2    3.000000    3.800000\n3    2.333333    6.333333\n4    3.000000    3.800000\n5    2.333333    6.333333\n6    3.000000    3.800000\n7    3.000000    3.800000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>mean_data1</th>\n      <th>mean_data2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.000000</td>\n      <td>3.800000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2.333333</td>\n      <td>6.333333</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3.000000</td>\n      <td>3.800000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2.333333</td>\n      <td>6.333333</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3.000000</td>\n      <td>3.800000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2.333333</td>\n      <td>6.333333</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>3.000000</td>\n      <td>3.800000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>3.000000</td>\n      <td>3.800000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 方法2，使用transform，分组后计算结果和原本的df保持一致\n",
    "k1_sum_tf = df_obj.groupby('key1').transform('mean').add_prefix('mean_')\n",
    "k1_sum_tf\n",
    "# df_obj[k1_sum_tf.columns] = k1_sum_tf\n",
    "# print(df_obj)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T03:41:01.550373300Z",
     "start_time": "2024-07-08T03:41:01.509427300Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [
    {
     "data": {
      "text/plain": "      data1     data2\n0  3.000000  0.200000\n1 -0.333333  2.666667\n2 -2.000000 -1.800000\n3 -0.333333 -4.333333\n4  1.000000  0.200000\n5  0.666667  1.666667\n6 -1.000000 -0.800000\n7 -1.000000  2.200000",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>data1</th>\n      <th>data2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3.000000</td>\n      <td>0.200000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.333333</td>\n      <td>2.666667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-2.000000</td>\n      <td>-1.800000</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.333333</td>\n      <td>-4.333333</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.000000</td>\n      <td>0.200000</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.666667</td>\n      <td>1.666667</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>-1.000000</td>\n      <td>-0.800000</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>-1.000000</td>\n      <td>2.200000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#实现a组和b组，谁比平均分高，谁比平均分低\n",
    "def diff_mean(s):\n",
    "    \"\"\"\n",
    "        返回数据与均值的差值，s传入的是某一个分组\n",
    "    \"\"\"\n",
    "    return s - s.mean()\n",
    "\n",
    "df_obj.groupby('key1').transform(diff_mean)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T06:33:52.548533600Z",
     "start_time": "2024-07-08T06:33:52.509250400Z"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "# 星际争霸--练习groupby的apply操作"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "outputs": [
    {
     "data": {
      "text/plain": "      LeagueIndex   Age  HoursPerWeek  TotalHours       APM\n0               5  27.0          10.0      3000.0  143.7180\n1               5  23.0          10.0      5000.0  129.2322\n2               4  30.0          10.0       200.0   69.9612\n3               3  19.0          20.0       400.0  107.6016\n4               3  32.0          10.0       500.0  122.8908\n...           ...   ...           ...         ...       ...\n3390            8   NaN           NaN         NaN  259.6296\n3391            8   NaN           NaN         NaN  314.6700\n3392            8   NaN           NaN         NaN  299.4282\n3393            8   NaN           NaN         NaN  375.8664\n3394            8   NaN           NaN         NaN  348.3576\n\n[3395 rows x 5 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>LeagueIndex</th>\n      <th>Age</th>\n      <th>HoursPerWeek</th>\n      <th>TotalHours</th>\n      <th>APM</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>5</td>\n      <td>27.0</td>\n      <td>10.0</td>\n      <td>3000.0</td>\n      <td>143.7180</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>5</td>\n      <td>23.0</td>\n      <td>10.0</td>\n      <td>5000.0</td>\n      <td>129.2322</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>4</td>\n      <td>30.0</td>\n      <td>10.0</td>\n      <td>200.0</td>\n      <td>69.9612</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>19.0</td>\n      <td>20.0</td>\n      <td>400.0</td>\n      <td>107.6016</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>3</td>\n      <td>32.0</td>\n      <td>10.0</td>\n      <td>500.0</td>\n      <td>122.8908</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>3390</th>\n      <td>8</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>259.6296</td>\n    </tr>\n    <tr>\n      <th>3391</th>\n      <td>8</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>314.6700</td>\n    </tr>\n    <tr>\n      <th>3392</th>\n      <td>8</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>299.4282</td>\n    </tr>\n    <tr>\n      <th>3393</th>\n      <td>8</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>375.8664</td>\n    </tr>\n    <tr>\n      <th>3394</th>\n      <td>8</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>348.3576</td>\n    </tr>\n  </tbody>\n</table>\n<p>3395 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dataset_path = './starcraft.csv'\n",
    "df_data = pd.read_csv(dataset_path, usecols=['LeagueIndex', 'Age', 'HoursPerWeek',\n",
    "                                             'TotalHours', 'APM'])\n",
    "df_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T06:36:04.890546900Z",
     "start_time": "2024-07-08T06:36:04.847773600Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Age  HoursPerWeek  TotalHours       APM\n",
      "LeagueIndex                                               \n",
      "1           2214  20.0          12.0       730.0  172.9530\n",
      "            2246  27.0           8.0       250.0  141.6282\n",
      "            1753  20.0          28.0       100.0  139.6362\n",
      "2           3062  20.0           6.0       100.0  179.6250\n",
      "            3229  16.0          24.0       110.0  156.7380\n",
      "            1520  29.0           6.0       250.0  151.6470\n",
      "3           1557  22.0           6.0       200.0  226.6554\n",
      "            484   19.0          42.0       450.0  220.0692\n",
      "            2883  16.0           8.0       800.0  208.9500\n",
      "4           2688  26.0          24.0       990.0  249.0210\n",
      "            1759  16.0           6.0        75.0  229.9122\n",
      "            2637  23.0          24.0       650.0  227.2272\n",
      "5           3277  18.0          16.0       950.0  372.6426\n",
      "            93    17.0          36.0       720.0  335.4990\n",
      "            202   37.0          14.0       800.0  327.7218\n",
      "6           734   16.0          28.0       730.0  389.8314\n",
      "            2746  16.0          28.0      4000.0  350.4114\n",
      "            1810  21.0          14.0       730.0  323.2506\n",
      "7           3127  23.0          42.0      2000.0  298.7952\n",
      "            104   21.0          24.0      1000.0  286.4538\n",
      "            1654  18.0          98.0       700.0  236.0316\n",
      "8           3393   NaN           NaN         NaN  375.8664\n",
      "            3373   NaN           NaN         NaN  364.8504\n",
      "            3372   NaN           NaN         NaN  355.3518\n"
     ]
    }
   ],
   "source": [
    "#apply比agg厉害的地方在于传参数\n",
    "def top_n(df, n=3, column='APM'):\n",
    "    \"\"\"\n",
    "        df是代表某个分组的数据，返回每个分组按 column 的 top n 数据\n",
    "    \"\"\"\n",
    "    return df.sort_values(by=column, ascending=False)[:n]\n",
    "\n",
    "print(df_data.groupby('LeagueIndex').apply(top_n,include_groups=False))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    },
    "ExecuteTime": {
     "end_time": "2024-07-08T06:40:14.476588600Z",
     "start_time": "2024-07-08T06:40:14.427552700Z"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "outputs": [
    {
     "data": {
      "text/plain": "                   Age  HoursPerWeek  TotalHours       APM\nLeagueIndex                                               \n1           3146  40.0          12.0       150.0   38.5590\n            3040  39.0          10.0       500.0   29.8764\n            721   38.0          16.0       300.0   71.5950\n            3298  37.0          12.0       300.0   22.0596\n2           920   43.0          10.0       730.0   86.0586\n            2437  41.0           4.0       200.0   54.2166\n            1175  41.0           8.0       250.0   54.2406\n            479   40.0          14.0       500.0   51.8358\n3           1258  41.0          14.0       800.0   77.6472\n            2972  40.0          10.0       500.0   60.5970\n            3176  40.0          16.0       580.0   46.8714\n            2882  39.0          12.0       450.0   53.7690\n4           1696  44.0           6.0       500.0   89.5266\n            1729  39.0           8.0       500.0   86.7246\n            1298  36.0          10.0       400.0   73.6980\n            1106  35.0          16.0       800.0  107.0412\n5           202   37.0          14.0       800.0  327.7218\n            2745  37.0          18.0      1000.0  123.4098\n            653   35.0          12.0       800.0  115.0644\n            3310  34.0           4.0       364.0   77.5512\n6           3069  31.0           8.0       800.0  133.1790\n            2706  31.0           8.0       700.0   66.9918\n            929   30.0          28.0      1500.0  123.3774\n            1240  29.0          20.0       800.0  141.6246\n7           2813  26.0          36.0      1300.0  188.5512\n            1992  26.0          24.0      1000.0  219.6690\n            1073  25.0          10.0       900.0  178.8342\n            320   25.0          42.0      2400.0  182.6208\n8           3340   NaN           NaN         NaN  189.7404\n            3341   NaN           NaN         NaN  287.8128\n            3342   NaN           NaN         NaN  294.0996\n            3343   NaN           NaN         NaN  274.2552",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th></th>\n      <th>Age</th>\n      <th>HoursPerWeek</th>\n      <th>TotalHours</th>\n      <th>APM</th>\n    </tr>\n    <tr>\n      <th>LeagueIndex</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">1</th>\n      <th>3146</th>\n      <td>40.0</td>\n      <td>12.0</td>\n      <td>150.0</td>\n      <td>38.5590</td>\n    </tr>\n    <tr>\n      <th>3040</th>\n      <td>39.0</td>\n      <td>10.0</td>\n      <td>500.0</td>\n      <td>29.8764</td>\n    </tr>\n    <tr>\n      <th>721</th>\n      <td>38.0</td>\n      <td>16.0</td>\n      <td>300.0</td>\n      <td>71.5950</td>\n    </tr>\n    <tr>\n      <th>3298</th>\n      <td>37.0</td>\n      <td>12.0</td>\n      <td>300.0</td>\n      <td>22.0596</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">2</th>\n      <th>920</th>\n      <td>43.0</td>\n      <td>10.0</td>\n      <td>730.0</td>\n      <td>86.0586</td>\n    </tr>\n    <tr>\n      <th>2437</th>\n      <td>41.0</td>\n      <td>4.0</td>\n      <td>200.0</td>\n      <td>54.2166</td>\n    </tr>\n    <tr>\n      <th>1175</th>\n      <td>41.0</td>\n      <td>8.0</td>\n      <td>250.0</td>\n      <td>54.2406</td>\n    </tr>\n    <tr>\n      <th>479</th>\n      <td>40.0</td>\n      <td>14.0</td>\n      <td>500.0</td>\n      <td>51.8358</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">3</th>\n      <th>1258</th>\n      <td>41.0</td>\n      <td>14.0</td>\n      <td>800.0</td>\n      <td>77.6472</td>\n    </tr>\n    <tr>\n      <th>2972</th>\n      <td>40.0</td>\n      <td>10.0</td>\n      <td>500.0</td>\n      <td>60.5970</td>\n    </tr>\n    <tr>\n      <th>3176</th>\n      <td>40.0</td>\n      <td>16.0</td>\n      <td>580.0</td>\n      <td>46.8714</td>\n    </tr>\n    <tr>\n      <th>2882</th>\n      <td>39.0</td>\n      <td>12.0</td>\n      <td>450.0</td>\n      <td>53.7690</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">4</th>\n      <th>1696</th>\n      <td>44.0</td>\n      <td>6.0</td>\n      <td>500.0</td>\n      <td>89.5266</td>\n    </tr>\n    <tr>\n      <th>1729</th>\n      <td>39.0</td>\n      <td>8.0</td>\n      <td>500.0</td>\n      <td>86.7246</td>\n    </tr>\n    <tr>\n      <th>1298</th>\n      <td>36.0</td>\n      <td>10.0</td>\n      <td>400.0</td>\n      <td>73.6980</td>\n    </tr>\n    <tr>\n      <th>1106</th>\n      <td>35.0</td>\n      <td>16.0</td>\n      <td>800.0</td>\n      <td>107.0412</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">5</th>\n      <th>202</th>\n      <td>37.0</td>\n      <td>14.0</td>\n      <td>800.0</td>\n      <td>327.7218</td>\n    </tr>\n    <tr>\n      <th>2745</th>\n      <td>37.0</td>\n      <td>18.0</td>\n      <td>1000.0</td>\n      <td>123.4098</td>\n    </tr>\n    <tr>\n      <th>653</th>\n      <td>35.0</td>\n      <td>12.0</td>\n      <td>800.0</td>\n      <td>115.0644</td>\n    </tr>\n    <tr>\n      <th>3310</th>\n      <td>34.0</td>\n      <td>4.0</td>\n      <td>364.0</td>\n      <td>77.5512</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">6</th>\n      <th>3069</th>\n      <td>31.0</td>\n      <td>8.0</td>\n      <td>800.0</td>\n      <td>133.1790</td>\n    </tr>\n    <tr>\n      <th>2706</th>\n      <td>31.0</td>\n      <td>8.0</td>\n      <td>700.0</td>\n      <td>66.9918</td>\n    </tr>\n    <tr>\n      <th>929</th>\n      <td>30.0</td>\n      <td>28.0</td>\n      <td>1500.0</td>\n      <td>123.3774</td>\n    </tr>\n    <tr>\n      <th>1240</th>\n      <td>29.0</td>\n      <td>20.0</td>\n      <td>800.0</td>\n      <td>141.6246</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">7</th>\n      <th>2813</th>\n      <td>26.0</td>\n      <td>36.0</td>\n      <td>1300.0</td>\n      <td>188.5512</td>\n    </tr>\n    <tr>\n      <th>1992</th>\n      <td>26.0</td>\n      <td>24.0</td>\n      <td>1000.0</td>\n      <td>219.6690</td>\n    </tr>\n    <tr>\n      <th>1073</th>\n      <td>25.0</td>\n      <td>10.0</td>\n      <td>900.0</td>\n      <td>178.8342</td>\n    </tr>\n    <tr>\n      <th>320</th>\n      <td>25.0</td>\n      <td>42.0</td>\n      <td>2400.0</td>\n      <td>182.6208</td>\n    </tr>\n    <tr>\n      <th rowspan=\"4\" valign=\"top\">8</th>\n      <th>3340</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>189.7404</td>\n    </tr>\n    <tr>\n      <th>3341</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>287.8128</td>\n    </tr>\n    <tr>\n      <th>3342</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>294.0996</td>\n    </tr>\n    <tr>\n      <th>3343</th>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>274.2552</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# apply函数接收的参数会传入自定义的函数中\n",
    "m=df_data.groupby('LeagueIndex').apply(top_n, n=4, column='Age',include_groups=False)\n",
    "m"
   ],
   "metadata": {
    "collapsed": false,
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     "text": [
      "C:\\Users\\Administrator\\AppData\\Local\\Temp\\ipykernel_7420\\4153032033.py:2: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  n=df_data.groupby('LeagueIndex', group_keys=False).apply(top_n,include_groups=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": "      LeagueIndex   Age  HoursPerWeek  TotalHours       APM\n2214            1  20.0          12.0       730.0  172.9530\n2246            1  27.0           8.0       250.0  141.6282\n1753            1  20.0          28.0       100.0  139.6362\n3062            2  20.0           6.0       100.0  179.6250\n3229            2  16.0          24.0       110.0  156.7380\n1520            2  29.0           6.0       250.0  151.6470\n1557            3  22.0           6.0       200.0  226.6554\n484             3  19.0          42.0       450.0  220.0692\n2883            3  16.0           8.0       800.0  208.9500\n2688            4  26.0          24.0       990.0  249.0210\n1759            4  16.0           6.0        75.0  229.9122\n2637            4  23.0          24.0       650.0  227.2272\n3277            5  18.0          16.0       950.0  372.6426\n93              5  17.0          36.0       720.0  335.4990\n202             5  37.0          14.0       800.0  327.7218\n734             6  16.0          28.0       730.0  389.8314\n2746            6  16.0          28.0      4000.0  350.4114\n1810            6  21.0          14.0       730.0  323.2506\n3127            7  23.0          42.0      2000.0  298.7952\n104             7  21.0          24.0      1000.0  286.4538\n1654            7  18.0          98.0       700.0  236.0316\n3393            8   NaN           NaN         NaN  375.8664\n3373            8   NaN           NaN         NaN  364.8504\n3372            8   NaN           NaN         NaN  355.3518",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>LeagueIndex</th>\n      <th>Age</th>\n      <th>HoursPerWeek</th>\n      <th>TotalHours</th>\n      <th>APM</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2214</th>\n      <td>1</td>\n      <td>20.0</td>\n      <td>12.0</td>\n      <td>730.0</td>\n      <td>172.9530</td>\n    </tr>\n    <tr>\n      <th>2246</th>\n      <td>1</td>\n      <td>27.0</td>\n      <td>8.0</td>\n      <td>250.0</td>\n      <td>141.6282</td>\n    </tr>\n    <tr>\n      <th>1753</th>\n      <td>1</td>\n      <td>20.0</td>\n      <td>28.0</td>\n      <td>100.0</td>\n      <td>139.6362</td>\n    </tr>\n    <tr>\n      <th>3062</th>\n      <td>2</td>\n      <td>20.0</td>\n      <td>6.0</td>\n      <td>100.0</td>\n      <td>179.6250</td>\n    </tr>\n    <tr>\n      <th>3229</th>\n      <td>2</td>\n      <td>16.0</td>\n      <td>24.0</td>\n      <td>110.0</td>\n      <td>156.7380</td>\n    </tr>\n    <tr>\n      <th>1520</th>\n      <td>2</td>\n      <td>29.0</td>\n      <td>6.0</td>\n      <td>250.0</td>\n      <td>151.6470</td>\n    </tr>\n    <tr>\n      <th>1557</th>\n      <td>3</td>\n      <td>22.0</td>\n      <td>6.0</td>\n      <td>200.0</td>\n      <td>226.6554</td>\n    </tr>\n    <tr>\n      <th>484</th>\n      <td>3</td>\n      <td>19.0</td>\n      <td>42.0</td>\n      <td>450.0</td>\n      <td>220.0692</td>\n    </tr>\n    <tr>\n      <th>2883</th>\n      <td>3</td>\n      <td>16.0</td>\n      <td>8.0</td>\n      <td>800.0</td>\n      <td>208.9500</td>\n    </tr>\n    <tr>\n      <th>2688</th>\n      <td>4</td>\n      <td>26.0</td>\n      <td>24.0</td>\n      <td>990.0</td>\n      <td>249.0210</td>\n    </tr>\n    <tr>\n      <th>1759</th>\n      <td>4</td>\n      <td>16.0</td>\n      <td>6.0</td>\n      <td>75.0</td>\n      <td>229.9122</td>\n    </tr>\n    <tr>\n      <th>2637</th>\n      <td>4</td>\n      <td>23.0</td>\n      <td>24.0</td>\n      <td>650.0</td>\n      <td>227.2272</td>\n    </tr>\n    <tr>\n      <th>3277</th>\n      <td>5</td>\n      <td>18.0</td>\n      <td>16.0</td>\n      <td>950.0</td>\n      <td>372.6426</td>\n    </tr>\n    <tr>\n      <th>93</th>\n      <td>5</td>\n      <td>17.0</td>\n      <td>36.0</td>\n      <td>720.0</td>\n      <td>335.4990</td>\n    </tr>\n    <tr>\n      <th>202</th>\n      <td>5</td>\n      <td>37.0</td>\n      <td>14.0</td>\n      <td>800.0</td>\n      <td>327.7218</td>\n    </tr>\n    <tr>\n      <th>734</th>\n      <td>6</td>\n      <td>16.0</td>\n      <td>28.0</td>\n      <td>730.0</td>\n      <td>389.8314</td>\n    </tr>\n    <tr>\n      <th>2746</th>\n      <td>6</td>\n      <td>16.0</td>\n      <td>28.0</td>\n      <td>4000.0</td>\n      <td>350.4114</td>\n    </tr>\n    <tr>\n      <th>1810</th>\n      <td>6</td>\n      <td>21.0</td>\n      <td>14.0</td>\n      <td>730.0</td>\n      <td>323.2506</td>\n    </tr>\n    <tr>\n      <th>3127</th>\n      <td>7</td>\n      <td>23.0</td>\n      <td>42.0</td>\n      <td>2000.0</td>\n      <td>298.7952</td>\n    </tr>\n    <tr>\n      <th>104</th>\n      <td>7</td>\n      <td>21.0</td>\n      <td>24.0</td>\n      <td>1000.0</td>\n      <td>286.4538</td>\n    </tr>\n    <tr>\n      <th>1654</th>\n      <td>7</td>\n      <td>18.0</td>\n      <td>98.0</td>\n      <td>700.0</td>\n      <td>236.0316</td>\n    </tr>\n    <tr>\n      <th>3393</th>\n      <td>8</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>375.8664</td>\n    </tr>\n    <tr>\n      <th>3373</th>\n      <td>8</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>364.8504</td>\n    </tr>\n    <tr>\n      <th>3372</th>\n      <td>8</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>NaN</td>\n      <td>355.3518</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group_keys为False后，被groupby的列不会变为索引\n",
    "n=df_data.groupby('LeagueIndex', group_keys=False).apply(top_n,include_groups=True)\n",
    "n"
   ],
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